CN119471569A

CN119471569A - A single-code-based position identification system, method, device and storage medium

Info

Publication number: CN119471569A
Application number: CN202510027102.0A
Authority: CN
Inventors: 马伟宇
Original assignee: Ningbo Kochi Intelligent Technology Co ltd
Current assignee: Ningbo Kochi Intelligent Technology Co ltd
Priority date: 2025-01-08
Filing date: 2025-01-08
Publication date: 2025-02-18
Anticipated expiration: 2045-01-08
Also published as: CN119471569B

Abstract

The present invention provides a position identification system, method, device and storage medium based on a single code channel, which relates to the technical field of position identification. The system includes: a transmitting module including at least four sending nodes, two adjacent sending nodes forming a sending node group, each sending node in the target sending node group and a sending node in the non-target sending node group forming a sending node pair; the single code channel module is used to fuse the initial signal data of each sending node in the sending node pair to obtain corresponding signal data; the receiving module is used to receive the signal data, identify and analyze each signal data to obtain analysis data; the calculation module is used to obtain the corresponding estimated distance according to the analysis data based on the path loss model; and then determine the position of the corresponding receiving module based on each analysis data. The present invention simplifies the complex multi-band configuration in the traditional positioning system. It makes the system design and implementation more efficient and reduces the hardware cost.

Description

Position identification system, method, equipment and storage medium based on single code channel

Technical Field

The invention relates to the technical field of position identification, in particular to a position identification system, a method, equipment and a storage medium based on a single code channel.

Background

In the current positioning technology, positioning systems in rooms and in complex environments face a number of challenges. Traditional positioning methods, such as GPS-based positioning, while performing well in open environments, have difficulty providing accurate location information in limited environments such as indoors or urban canyons. This is mainly due to the weakening of the GPS signal and the influence of multipath effects, resulting in a significant decrease in positioning accuracy.

Furthermore, some related wireless location systems typically rely on complex configurations of multiple frequency bands, multiple antennas, which not only increases the cost and complexity of the system, but also makes maintenance and management of the system more difficult. In some application scenarios, especially in fields with high requirements on real-time performance and economy (such as smart home, industrial automation, logistics management, etc.), related technologies are difficult to meet the needs of users.

In terms of signal transmission, the related art mostly uses signal strength (RSSI) for distance estimation, but due to environmental changes (such as obstacles, personnel movement, etc.), fluctuations in signal strength may cause inaccuracy in distance estimation. Furthermore, the lack of an efficient signal quality assessment mechanism makes priority processing and weighting calculations of signals difficult to implement in a multi-node environment, further affecting the accuracy and reliability of positioning.

Therefore, a new positioning system is needed to provide high-precision position information in a complex environment, simplify the system architecture, reduce the cost, and improve the level of intelligence of signal processing.

Disclosure of Invention

The invention solves the problem of simplifying a position recognition system and improving measurement accuracy.

In order to solve the above problems, the present invention provides a position identifying system, method, device and storage medium based on single code channel.

In a first aspect, the invention provides a position identification system based on a single code channel, which comprises a transmitting module, a single code channel module, at least one receiving module and a calculating module;

the transmitting module comprises at least four transmitting nodes, two adjacent transmitting nodes form a group of transmitting node groups, the transmitting node groups transmit signal data to each receiving module through a single code channel module, and any transmitting node in a target transmitting node group and any transmitting node in a non-target transmitting node group form a transmitting node pair;

The transmitting module comprises at least four transmitting nodes, wherein each transmitting node transmits initial signal data to the receiving module through a single code channel module according to a preset time interval, two adjacent transmitting nodes form a group of transmitting node groups, and each transmitting node in a target transmitting node group and one transmitting node in a non-target transmitting node group form a transmitting node pair respectively;

the single code channel module is used for fusing initial signal data of each sending node in the sending node pair to obtain signal data corresponding to the sending node pair;

the receiving module is used for receiving the signal data of each sending node pair, and carrying out identification analysis on each signal data to obtain analysis data;

The calculation module is used for obtaining a corresponding estimated distance according to each analysis data based on a path loss model, and determining the position of the corresponding receiving module according to each analysis data and the corresponding estimated distance.

Optionally, the initial signal data sent by each sending node is generated according to a corresponding preset waveform parameter, the preset waveform parameter corresponding to each sending node is different, and the preset waveform parameter includes one or more of amplitude, frequency and duration of a waveform signal.

Optionally, the signal data includes signal strength data of initial signal data of two sending nodes in the sending node pair, and the receiving module is specifically configured to:

Separating the signal data to obtain two temporary signal data and two signal intensity data;

Matching the two temporary signal data with preset waveforms corresponding to different preset waveform parameters respectively, and determining the sending nodes corresponding to the two temporary signal data respectively;

And comparing the signal strengths of the two temporary signal data, and determining the signal strength data corresponding to the sending node corresponding to the temporary signal data according to a comparison result, wherein the analysis data comprise the sending node corresponding to the temporary signal data and the signal strength data thereof.

Optionally, the single code channel-based position recognition system further comprises an environment sensing module;

the environment sensing module is used for collecting environment change data, extracting characteristics of the environment change data to obtain environment characteristic data, and adjusting parameters of the path loss model based on the environment characteristic data.

Optionally, the performing parameter adjustment on the path loss model based on the environmental characteristic data includes:

determining the ideal signal strength of the corresponding transmitting node received by the receiving module according to the environmental characteristic data;

determining corresponding error data according to the ideal signal intensity of each transmitting node and the corresponding signal intensity data;

acquiring a preset threshold value, and judging each error data based on the preset threshold value;

and when the error data does not meet the preset threshold value, carrying out parameter adjustment on the path loss model according to the current environment characteristic data.

Classifying the environmental characteristic data to obtain first characteristic data and second characteristic data;

judging whether the first characteristic data corresponds to the received signal strength smaller than a first preset value or judging whether the second characteristic data corresponds to the received signal strength larger than a second preset value;

Parameter adjustment is carried out on the path loss model according to the first characteristic data and the second characteristic data;

The received signal strength is the ideal signal strength of the corresponding transmitting node received by the receiving module.

Optionally, the analysis data further includes location data of each transmitting node in the pair of transmitting nodes, and the determining the location of the corresponding receiving module according to each analysis data and the corresponding estimated distance further includes:

determining corresponding signal-to-noise ratio data based on the signal strength data in the analysis data, and determining corresponding weight data of the transmitting node based on the signal strength data and the corresponding signal-to-noise ratio data;

And determining the position of the receiving module based on the weight data of each transmitting node, the corresponding position data and the estimated distance.

In a second aspect, the present invention further provides a single code channel-based position identification method, which is applied to the single code channel-based position identification system, where the single code channel-based position identification method includes:

each sending node is controlled to send initial signal data according to a preset time interval;

The method comprises the steps of merging initial signal data of each sending node in a sending node pair to obtain signal data corresponding to the sending node pair, wherein the sending node pair consists of each sending node in a target sending node group and one sending node in a non-target sending node group, and the target sending node group consists of two adjacent sending nodes;

carrying out identification analysis on each signal data to obtain analysis data;

And determining the position of a corresponding receiving module according to each analysis data and the corresponding estimated distance.

In a third aspect, the present invention also provides an electronic device, including a memory and a processor;

The memory is used for storing a computer program;

The processor is configured to implement the single code channel based location identification method as described above when executing the computer program.

In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a single code channel based position identification method as described above.

The position identification system, method, equipment and storage medium based on the single code channel have the beneficial effects that:

the transmitting module comprises at least four transmitting nodes, which form a group of transmitting nodes according to two adjacent nodes. Each sending node sends signal data according to a preset time interval, so that the order and stability of signal transmission are ensured, and any sending node in the target sending node group can form a sending node pair with any sending node in the non-target sending node group, so that diversified signal transmission and interaction are realized.

The single code channel module fuses the initial signal data from the transmitting node pair. Two adjacent transmitting nodes form a group of transmitting node groups, and the target transmitting node group is an arbitrarily selected transmitting node group. By fusing the signal data, the reliability and accuracy of the signal can be improved.

The receiving module is used for receiving signal data from each sending node, identifying and analyzing the signals and extracting useful information.

The calculation module calculates an estimated distance between the receiving module and the transmitting node based on the path loss model. And determining the specific position of the receiving module according to the analysis data and the estimated distance.

Therefore, the invention performs accurate distance estimation by combining a plurality of transmitting nodes and signal data fusion and utilizing a path loss model. The method can reduce the positioning error caused by signal interference or attenuation of a single node, thereby improving the positioning accuracy, and the design of a plurality of transmitting nodes can effectively position the system through other nodes under the condition that some nodes fail or the signal quality is reduced, so that the robustness of the system is enhanced. And because the transmitting nodes within the same node group are adjacent, they are subject to relatively similar environmental conditions (e.g., temperature, humidity, electromagnetic interference, etc.) and may experience similar signal attenuation. Therefore, the transmitting nodes of different node groups are constructed into transmitting node pairs, and the signals of the nodes are fused, so that the overall strength of the signals can be improved, the problem that a single signal is weaker is solved, and the stability and the reliability of the signals are improved. And because the nodes in each sending node group and the nodes in the non-self group form a sending node pair, the design can reduce the synchronous influence of the same environmental factor on all signals, thereby improving the anti-interference capability of the system.

Meanwhile, the single code channel module fuses initial transmission signals of the transmission nodes in the transmission node pair to obtain fused signals, noise and interference can be effectively reduced through fusing the initial transmission signals, signal strength is enhanced, signal quality is remarkably improved, and for example, when signals of one node are weak or fail, information provided by other nodes can be still relied on, so that higher system robustness is realized. And the fused signals can resist environmental noise and interference, and the usability and accuracy in a complex environment are improved. Signal fusion algorithm flexibility different signal fusion algorithms, such as weighted average, kalman filtering, etc., can be used to adapt to different application requirements and channel conditions. The dynamic control mechanism can effectively reduce signal collision and interference and optimize the quality of signal transmission.

The system adopts the combination of a single code channel module and a plurality of transmitting nodes, and simplifies the complex multi-band and multi-antenna configuration in the traditional positioning system. This simplification makes the system design and implementation more efficient, reducing hardware costs and system maintenance difficulties. The system is easy to deploy and manage, and is suitable for being widely applied to different scenes.

Finally, the receiving module can receive and analyze the signal data from each sending node pair in real time and quickly generate analysis data. The real-time performance enables the system to respond to environmental changes rapidly and adapt to positioning requirements in dynamic scenes. And through analyzing the signal data of each sending node, the system can identify and screen out the signal with higher quality, thereby improving the positioning accuracy. In combination with the path loss model, the system can better cope with the influence of environmental changes (such as obstacles, interference and the like). The stable positioning can still be realized in a complex environment, and the overall reliability of the system is improved.

In summary, the single code channel-based position identification system has the advantages of remarkably improving the performance and applicability of the traditional positioning technology through the advantages of high-efficiency positioning precision, simplified system architecture, flexible node configuration, dynamic signal control, real-time data analysis, enhanced signal quality processing and the like. The technical scheme not only meets the requirements of the current market on an efficient, economical and reliable positioning system, but also provides a solid foundation for future intelligent positioning application. And the design of the system makes the system suitable for various application scenes, including indoor positioning, industrial automation, intelligent home, logistics management and the like. The flexibility and the high efficiency of the system enable the system to meet the positioning requirements of different industries.

Drawings

FIG. 1 is one example diagram of a single code channel based location identification system in accordance with an embodiment of the present invention;

FIG. 2 is a second exemplary diagram of a single code channel based position identification system according to an embodiment of the present invention;

FIG. 3 is a flowchart of a single code channel based position identification method according to an embodiment of the present invention;

fig. 4 is an exemplary diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. While the invention is susceptible of embodiment in the drawings, it is to be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the invention. It should be understood that the drawings and embodiments of the invention are for illustration purposes only and are not intended to limit the scope of the present invention.

It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.

The term "comprising" and variations thereof as used herein is meant to be open-ended, i.e., "including but not limited to," based at least in part on, "one embodiment" means "at least one embodiment," another embodiment "means" at least one additional embodiment, "some embodiments" means "at least some embodiments," and "optional" means "optional embodiment. Related definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish between different devices, modules or units, and are not intended to limit the order or interdependence of functions performed by these devices, modules or units.

It should be noted that references to "a" and "an" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of such messages or information.

In the related art, a controller analyzes and processes response information returned by a receiving device according to a received response signal, so as to determine the position of the receiving device, the response information is transmitted on a code channel, and the controller screens the response information, so as to determine the position of the receiving device. Although this technical solution determines the position of the signal, there is a problem that the receiving device is complicated to install and the position identification information cannot be judged due to the limitation of the transmission distance.

Based on the above problems, the present embodiment provides a position identifying system, method, device and storage medium based on single code channel.

As shown in FIG. 1, the position identification system based on the single code channel provided by the embodiment of the invention comprises a transmitting module, a single code channel module, at least one receiving module and a calculating module.

The transmitting module comprises at least four transmitting nodes, each transmitting node transmits initial signal data to the receiving module through the single code channel module according to a preset time interval, two adjacent transmitting nodes form a group of transmitting node groups, each transmitting node in a target transmitting node group and one transmitting node in a non-target transmitting node group form a transmitting node pair, and the target transmitting node group is any transmitting node group.

Specifically, the transmitting module comprises at least four transmitting nodes. The nodes are distributed within a particular area. Each transmitting node is responsible for periodically transmitting signals so that the receiving module can acquire the positioning information. Each transmitting node communicates with the receiving module through a single code channel module. The single code channel module is used for managing the sending and receiving of signals and ensuring the effective transmission of the signals. Each transmitting node transmits initial signal data to the receiving module through the single code channel module at preset time intervals (e.g., every several milliseconds). The timing transmission mechanism ensures the continuity and real-time performance of signals.

Two adjacent transmitting nodes form a group of transmitting nodes. In this example, for example, transmitting nodes A, B, C and D, the transmitting node groups 1:A and B (adjacent) and the transmitting node groups 2:D and C (adjacent) may be formed. The target sending node group refers to a sending node group selected at any time, and the system can dynamically select according to the needs. For example, group 1 (a and B) may be selected as the target transmitting node group.

In the target transmitting node group, each transmitting node forms a transmitting node pair with one transmitting node in the non-target transmitting node group. For example, if the target set of transmitting nodes is (A and B), the transmitting node pair may be A and C, B and D. The configuration mode ensures that each sending node can form pairing with other nodes, thereby increasing redundancy and reliability of signal transmission and providing a basis for the subsequent fusion process. Similar signal attenuation may be experienced when the environmental conditions (e.g., temperature, humidity, electromagnetic interference, etc.) they face are relatively similar due to the proximity of transmitting nodes within the same node group. Therefore, the transmitting nodes of different node groups are constructed into transmitting node pairs, and the signals of the nodes are fused, so that the overall strength of the signals can be improved, and the problem that a single signal is weaker is solved.

And the dynamic selection of the target sending node group enables the system to adjust the signal sending strategy according to the real-time environment change (such as personnel movement, obstacle change and the like), so that the adaptability of the system is improved.

And the single code channel module is used for fusing the initial signal data of each sending node in the sending node pair to obtain the signal data corresponding to the sending node pair.

Specifically, the single code channel module is used as a key component of the position identification system and is responsible for processing signal data from the transmitting node pair. The main task of the method is to conduct fusion processing on signals so as to improve the reliability and accuracy of the signals.

Each transmitting node (e.g., node a and node B) in the transmitting node pair transmits initial signal data at preset time intervals. The single-code channel module receives signal data from the transmitting nodes. These signals may include information of different intensities, phases, frequencies, etc.

The single code channel module performs fusion processing on signal data from the same transmitting node pair by using a specific algorithm (such as weighted average, filtering and the like). The process may include denoising, which removes noise due to multipath propagation, making the signal clearer. Weighted fusion, namely, giving different weights to different signals according to the quality (such as signal strength) of the signals, so as to ensure that more reliable signal data is better considered. Compensation and correction-the compensation process is performed on the signal taking into account path loss and other environmental factors in the signal propagation.

After fusion processing, the single code channel module generates corresponding fusion signal data. These data reflect the combined signal information of the transmitting node pair, typically with higher signal-to-noise ratio and reliability.

And finally, the fused signal data is transmitted to a receiving module for subsequent analysis and positioning calculation. In order to avoid the change of the signal intensity of the separated signals obtained after the fusion and the separation, the signal intensity of the initial signal data of each starting node pair in the sending node pairs can be expressed by a marker in the signal fusion process and combined together to be added into the fused signal data.

By fusing signals from different sending nodes, the Shan Ma-channel module can effectively reduce uncertainty caused by signal interference and attenuation, so that the overall reliability of the signals is improved. And the fused signals can more accurately reflect the information of the real position, so that the accuracy of position estimation is improved, and particularly when the environment is complex or the signals are weaker. And the single code channel module can enhance the capability of the system to resist noise and interference by fusing the data of a plurality of signals, such as signal interference caused by other electronic equipment or environmental factors.

By fusing a plurality of signals, fluctuation and instability caused by a single signal are reduced, and continuous and effective operation of the whole system in a changeable environment is ensured. The fused signal data is small in data quantity, and bandwidth requirements can be reduced in the transmission process, so that the data transmission efficiency of the whole system is improved.

specifically, the receiving module receives signal data from a plurality of sending node pairs (such as a sending node pair consisting of a and B, and a sending node pair consisting of C and D) according to a preset communication protocol.

The signal data is obtained by fusion of initial transmission signals based on the included transmission nodes. For example, the fusion formula may be expressed as:

;

Wherein, Representing the ambient noise and,As the data of the signal, there is provided,For the initial transmission signal of the transmitting node a,For transmitting the initial transmission signal of node C.

Noise and interference can be effectively reduced, signal strength is enhanced, and signal quality is remarkably improved by fusing initial transmission signals, for example, when the signal of one node is weak or fails, information provided by other nodes can be relied on, and higher system robustness is realized. And the fused signals can resist environmental noise and interference, and the usability and accuracy in a complex environment are improved.

The initial signal data may include information such as node ID, transmission time, signal strength, etc. The receiving module temporarily stores the received signal data in a buffer memory for subsequent processing. The time stamp of each received packet is also recorded to ensure that the time series information of the signal can be analyzed.

The receiving module uses a signal processing algorithm to identify each received signal data. This may involve first performing separate identification of each signal data. Such as signal strength analysis, which analyzes the strength and quality of the signal and removes low quality signal data. And (3) denoising the signal, namely reducing the influence of background noise by using a filtering technology and improving the definition of the signal. Signal decoding, namely extracting effective information in the signal, such as generating position coordinates, unique identification of a transmitting node and the like. The receiving module performs analysis based on the identified signal data to generate analysis data.

Therefore, the receiving module can effectively process and analyze the signal data of a plurality of sending nodes, and the overall recognition efficiency of the system on the signals is improved. When the receiving module is able to analyze and filter out low quality or interfering signals, the anti-interference capability of the system is enhanced. This ensures that the positioning system provides stable service even in environments with multiple sources of signals and interference factors. And the receiving module can integrate the data of a plurality of sending nodes, so that the system can carry out comprehensive judgment according to the information of a plurality of sources. By comparing and analyzing the multi-node information, more comprehensive service can be provided for users.

Meanwhile, the real-time receiving and analyzing of the signals enable the system to quickly respond to environmental changes, such as the movement of a user and the change of signal intensity, and ensure instant and accurate positioning and navigation services.

Specifically, the computing module receives signal analysis data from the receiving module, which typically includes signal strength information for each transmitting node and location information for the node (e.g., coordinates of the node).

The path loss model is used to describe the law of signal strength variation with distance during signal propagation. Common path loss models include free space models, log normal fading models, live environment models, and the like.

And selecting a proper path loss model according to the application scene. For example, a lognormal fading model may be used in an indoor environment, while a free space model may be used in an open area. The calculation module calculates an estimated distance between the receiving module and each transmitting node using the received signal strength (e.g., RSSI) data and the path loss model. The estimated distance information and the known location of the transmitting node are then used to determine the specific location of the receiving module using triangulation or least squares. This procedure requires distance information of at least three (ideally at most) transmitting nodes to ensure accuracy and reliability of their positioning. The calculation module transmits the final estimated position results back to other parts of the system (e.g., the user interface or decision support system) for corresponding operation or feedback.

For example, recording signal strength information for each signal, the path loss model may be expressed as:

;

Wherein, Is the loss value per unit distance (typically 1 meter), n is the path loss index reflecting the environmental impact, typically depending on the propagation environment (e.g., open ground, city, indoor, etc.), d is the distance between the transmitting node and the receiving module (in meters), and X is other influencing factors such as multipath effects or environmental interference.

From the received signal strength and path loss model, the following relationship may be set:

;

Wherein, For the received signal strength (dBm),For the transmit power (dBm) of the transmitting node,For the path loss at a given distance d.

By rearranging the path loss equation we can solve for the distance d:

。

By utilizing the path loss model and the signal intensity data, the calculation module can efficiently estimate the distance of the receiving module, thereby improving the positioning accuracy of the whole positioning system. Under different environmental conditions, a proper path loss model can be selected according to actual needs, so that the system can flexibly adapt to various scenes (such as indoor, outdoor and complex environments) and provide stable positioning service. And the position is calculated by combining the distance information from a plurality of sending nodes through a comprehensive algorithm, so that the influence caused by random fluctuation of single node signals can be effectively restrained, and the anti-interference capability of the system is enhanced.

The computing module can rapidly process the received data and update the position in real time, and ensure that the positioning information of the user or the equipment is up to date, thereby improving the response speed of the system and adapting to the rapidly-changing scene requirement. The accurate positioning capability enables the system to be widely applied to various scenes such as navigation, asset tracking, personnel positioning, environment monitoring and the like, and promotes the development of intelligent application.

Such a location identification system is deployed in, for example, a shopping mall. The layout of the shopping center is such that the transmitting nodes are set up in four areas of the shopping center (entrance, checkout counter, merchandise area and rest area) respectively, labeled A, B, C and D. Each two adjacent transmitting nodes form a group of transmitting nodes, namely groups 1:A and B, groups 2:B and C, groups 3:C and D, groups 4A and D (forming a ring layout), and the transmitting module controls each transmitting node (A, B, C and D) to transmit signal data at preset time intervals (for example, every 100 milliseconds). The data transmitted by each transmitting node includes information such as node ID, time stamp, and signal strength. The receiving module receives signal data from each transmitting node in real time, and identifies and analyzes the signals. Meanwhile, the receiving module records the signal intensity of each node, performs denoising processing and reserves effective signals. The method comprises the steps of receiving signal strength data received by a receiving module, wherein the signal strength data comprise 40dBm of a node A, 45dBm of a node B, 50dBm of a node C and 55dBm of a node D, and estimating the distance between the receiving module and each sending node by a calculating module according to the received signal strength data by using a path loss model.

The re-calculation module uses the estimated distance and the known position of the transmitting node to determine the specific position of the receiving module by triangulation or least square method.

In this embodiment, by combining a plurality of transmitting nodes and signal data fusion, accurate distance estimation is performed by using a path loss model. The method can reduce the positioning error caused by signal interference or attenuation of a single node, thereby improving the positioning accuracy, and the design of a plurality of transmitting nodes can effectively position the system through other nodes under the condition that some nodes fail or the signal quality is reduced, so that the robustness of the system is enhanced. And similar signal attenuation may be experienced when the environmental conditions (e.g., temperature, humidity, electromagnetic interference, etc.) they face are relatively similar due to the proximity of transmitting nodes within the same node group. Therefore, the transmitting nodes of different node groups are constructed into transmitting node pairs, and the signals of the nodes are fused, so that the overall strength of the signals can be improved, the problem that a single signal is weaker is solved, and the stability and the reliability of the signals are improved. And because the nodes in each sending node pair are paired with the nodes in the non-self group, the design can reduce the synchronous influence of the same environmental factor on all signals, thereby improving the anti-interference capability of the system.

Specifically, each transmitting node generates initial signal data according to corresponding preset waveform parameters. These parameters include one or more of amplitude, frequency, and duration of the waveform signal, and the parameters of each transmitting node may be different. For example, the preset waveform parameter of the transmitting node A is 10V in amplitude, 2kHz in frequency and 100ms in duration. The preset waveform parameters of the sending node B are that the amplitude is 8V, the frequency is 1kHz, and the duration is 150ms.

The receiving module receives signal data from a transmitting node pair (e.g., transmitting nodes a and B) and separates the received signal data to obtain two temporary signal data and two corresponding signal strength data. The separation process takes into account that signals that may be transmitted by different nodes may mix due to signal overlap.

And respectively matching the obtained two temporary signal data with preset waveforms corresponding to different preset waveform parameters. In the matching process, the receiving module compares the temporary signal data with the shape, amplitude and frequency of the preset waveform. And determining a transmitting node corresponding to each temporary signal data. For example, if the temporary signal data matches node a corresponding to a preset waveform parameter, the signal may be considered to belong to transmitting node a.

The signal strengths of the two temporary signal data are compared. The receiving module analyzes the two signal intensity data, finds out the difference, and determines the final value of the signal intensity data of the transmitting node corresponding to the temporary signal data according to the comparison result. For example, if the signal strength of one temporary signal data is greater than the signal strength of the other temporary signal data, then it corresponds to the greater of the two divided signal strength data. Thus, the analysis data includes the corresponding signal strength data for each transmitting node and its corresponding temporary signal data.

The analysis data is finally output, including the identification (temporary signal data) of each transmitting node and the corresponding signal strength data thereof, for subsequent calculation and positioning.

By using different preset waveform parameters, signals from different sending nodes can be effectively distinguished from various signals, the independence of signal data and the accuracy of analysis are improved, and the positioning accuracy of a system is further improved.

And the signal matching process effectively reduces interference caused by aliasing signals, and enhances the adaptability of the system to complex signal environments. In a signal superposition or interference environment, the system can still reliably identify the signals of each transmitting node. And the preset waveform parameters of different sending nodes allow the system to be flexibly configured, and can be adjusted according to specific application scenes and user requirements so as to optimize signal transmission and identification performance.

Because the fused signal data is obtained by fusing the initial signal data of the two sending nodes in the sending node pair, compared with the two sending nodes in the same sending node group, the distance between the two sending nodes in the sending node pair is farther, so that the difference of environmental conditions facing the two sending nodes is larger, the difference of the initial signal data of the two sending nodes in the sending node pair is larger, separation is facilitated, the separation precision is higher, and the positioning precision of a subsequent receiving module is higher.

Under the condition of external interference or signal attenuation, the accurate positioning of the position can be still maintained through the comparison and analysis of the signal intensity, so that the whole system is more stable. And the signal separation and matching are combined, so that the requirement on the complexity of a signal processing algorithm is reduced, multiple signals can be effectively processed, and the efficiency and the speed of data processing are improved. And through timely analyzing and comparing signal intensity and temporary signal data, the receiving module can rapidly provide position information and adapt to application scenes (such as automatic driving, unmanned aerial vehicle navigation and the like) needing real-time feedback.

The signal data analysis flow combines the functions of the receiving module with the characteristics of the sending node, so that signals from different sources can be effectively distinguished and identified, and the performance and the reliability of the system are obviously improved. The method has flexibility and adaptability, and provides powerful technical support for various information systems needing accurate positioning.

Specifically, as shown in fig. 2, in a single code channel-based position recognition system, the introduction of an environmental sensing module can significantly improve the accuracy and adaptability of the system. The environment sensing module is responsible for collecting real-time environment change data, extracting features based on the data and finally generating environment feature data.

The environment sensing module monitors the change of the surrounding environment and collects environment data such as temperature, humidity, illumination intensity, object shielding condition, electromagnetic interference and the like. And analyzing the collected data to extract features related to environmental changes. These characteristics may include information such as ambient noise levels, the presence of dynamic obstructions, and weather changes.

The environmental sensing module obtains data from different sensors. For example, the temperature sensor measures the current temperature to be 25 ℃, the humidity sensor measures the humidity to be 60%, and the illumination sensor measures the illumination intensity to be 300 lux. The raw environmental data is converted into feature data that can be used for further analysis. For example, depending on temperature and humidity, a "dry" or "wet" state is extracted, which may affect signal propagation.

A data set is formed containing environmental characteristics describing the state of the current environment. This may be a vector, for example:;

Based on the extracted environmental feature data, parameters of the path loss model are adjusted. Conventional path loss models (e.g., hata models or Okumura models) may require optimization based on actual environmental conditions. The loss factor of signal propagation can be adjusted here by introducing environmental characteristic factors.

It is assumed that the signal loss increases by 10% in case of high humidity, whereas the signal loss may decrease by 5% in case of good lighting conditions (e.g. under direct sunlight). During the analysis, if the current environmental state is "wet", the adjusted path loss model may be as follows:;

by dynamically adjusting the path loss model, the signal propagation can be predicted more accurately according to the environmental change, and the accuracy of distance estimation is improved, namely the system can reflect the influence of the environment more accurately, so that the accuracy of receiving module position estimation is improved.

In a complex environment (such as a shopping center, an exhibition or a region blocked by a machine-warping object), the environmental characteristics can reflect dynamic changes in time, so that the system can work normally under various conditions. And the real-time feedback of the environmental change data enables the system to be correspondingly adjusted according to the change so as to carry out intelligent environmental adaptation. The intelligent improvement not only can optimize positioning, but also can promote user experience. The collected environmental characteristic data can be used for training a machine learning model, so that the self-adaptive capacity and learning capacity of the system are further improved, and the system is continuously optimized under different environmental conditions.

In summary, the value of the environmental sensing module to the single code channel based position recognition system is improved, the precision and reliability are improved, and the intelligent and self-adaptive capabilities of the system are improved, so that the system has more practicability in a variable environment.

Specifically, prior to making the path loss model parameter adjustments, relevant environmental characteristic data first needs to be collected. Such data may include geographical information of the environment, distribution of objects (e.g., buildings, trees, etc.), signal propagation characteristics (e.g., frequency, reflection, diffraction, etc.), and weather conditions (e.g., wind speed, rain, etc.).

And according to the collected environmental characteristic data, the receiving module calculates ideal signal strength of the transmitting node under specific environmental conditions. Ideal signal strength refers to the theoretical strength that a signal can reach without any interference and loss. The received signal strength data is compared with the corresponding ideal signal strength to determine error data. The error data may be calculated by the following formula:

Error data = ideal signal strength Received signal strength data;

the calculation of the error data is to facilitate the assessment of the loss suffered by the signal during propagation.

The system sets a preset threshold value that is used to determine whether the error data is within an acceptable range. The establishment of the preset threshold is usually based on experience or previous actual measurements, with the aim of identifying the degree of degradation of the signal quality. And judging each calculated error data, and determining whether the error data meets a preset threshold value. If the error data is within the preset threshold value range, the signal strength is stable and meets the expectations, and if one error data is out of range, the problem of obvious path loss is caused.

When the error data does not meet the preset threshold, the receiving module can carry out parameter adjustment on the path loss model according to the current environmental characteristic data. Adjusting the content may include adjusting the signal attenuation coefficient, and adjusting the effect of environmental factors (e.g., the reflective characteristics of an obstacle) on the signal. The adjustment aims at optimizing the model to enable the model to reflect the actual signal propagation situation more accurately, so that the positioning accuracy is improved.

By adjusting the path loss model in real time, the system can reflect the signal propagation characteristics in the actual environment more accurately, thereby remarkably improving the positioning accuracy. The process can dynamically adapt to different environmental conditions (such as city and country, daytime climate change and the like), and improve the performance under various complex environments. At the same time, the path loss model can be monitored and adjusted in real time, which is particularly important for applications requiring immediate feedback and high availability (e.g., autopilot, intelligent transportation).

When the signal intensity deviation is identified, the model parameters can be adjusted in time, so that risks of misjudgment and mispositioning can be reduced, and the efficiency and accuracy of signal processing are improved. And environmental factors are considered in decision, the power of signal transmission and the sensitivity of signal reception can be more reasonably distributed, so that the use of system resources is optimized, and the energy consumption is reduced. Through continuous environment monitoring and dynamic adjustment of model parameters, the system can continuously learn and optimize by itself, and a sustainable improved feedback mechanism is formed.

According to the process, the parameter adjustment is carried out on the path loss model based on the environmental characteristic data, so that the positioning accuracy and the adaptability of the system can be remarkably improved. The dynamic identification and real-time updating of the environmental factors enable the system to better cope with different working environments, thereby providing more reliable positioning service for various application scenes.

Specifically, the receiving module first acquires and collects environmental characteristic data. Such data may include different types of signal interference sources, types and layout of obstructions, characteristics of geographic locations, and so forth. And classifying the environmental characteristic data to obtain first characteristic data and second characteristic data. For example, the first characteristic data may correspond to a particular type of obstacle (e.g., a tall building) affecting signal strength. The second characteristic data may correspond to environmental conditions (e.g., weather changes, etc.), which may have an effect on the signal propagation state.

The system judges whether the first characteristic data corresponds to the received signal strength smaller than a first preset value. If the signal strength is below this value, it may be indicative of a severe attenuation of the signal within the range of influence of the characteristic data.

And meanwhile, judging whether the second characteristic data corresponds to the received signal strength being larger than a second preset value. If the signal strength is above this value, it may be indicative that the signal is well-propagated within the range of influence of the characteristic data.

And according to the judgment result, adjusting parameters of the path loss model:

If the signal strength corresponding to the first characteristic data is lower than the first preset value, the signal attenuation coefficient may need to be increased or the environmental parameters in the model may need to be adjusted to be more suitable for the current environmental characteristic.

If the signal strength corresponding to the second characteristic data is higher than the second preset value, it may be necessary to correspondingly reduce the signal attenuation parameters in the path loss model or to enhance the consideration of certain propagation conditions.

The received signal strength here refers to the ideal signal strength of the corresponding transmitting node received by the receiving module, which is the strength that the signal should be able to reach without other interference.

By classifying and analyzing the environmental characteristic data, the parameters of the path loss model can be more accurately adjusted, so that the parameters better reflect the actual signal propagation conditions, and the accuracy of the whole system is improved. Meanwhile, the system can dynamically adjust the path loss model according to environmental changes (such as different obstacles, weather conditions and the like), so that the adaptability of the system is improved. And by identifying and processing the situation that the signal strength is lower than the first preset value or higher than the second preset value, the system can actively optimize the signal propagation route, and ensure that the signals between all the sending nodes are more reliable and stable.

In high-demand environments (such as indoor positioning, wireless communication and the like), better quality service can be provided through effective path loss model adjustment, and connection problems or positioning errors of users in the using process are reduced. Because the system can automatically adjust the path loss model in real time, the manual intervention and the maintenance requirement of equipment are reduced, and the overall maintenance cost is reduced. Meanwhile, through continuous parameter adjustment and environment reaction, the system comprises a self-learning module, and can gradually improve the performance of the system under different environment conditions, so that a more intelligent response mechanism is formed.

The process improves the practical application performance of the path loss model through the classification of the environmental characteristic data and the judgment of the signal strength. The method not only can optimize signal propagation and positioning effects, but also can adapt to different operating environments and improve the overall performance of the system.

The scheme allows the system to respond to dynamic and static factors in real time, and model parameters can be flexibly adjusted through classification of environmental characteristic data, so that stability and accuracy of signals are ensured. And by dynamically adjusting parameters of the path loss model, the signal attenuation caused by environmental change can be effectively reduced, the signal receiving quality and positioning accuracy are improved, and the probability of misjudgment and failure is reduced. Meanwhile, the system is ensured to more effectively utilize limited signal resources under different conditions, unnecessary energy consumption and interference are reduced, and the overall efficiency of equipment and a system is improved. While providing more reliable signaling and accurate location services for end users, improving the experience of using smart devices (such as navigation systems), especially in complex and dynamic environments such as malls, blocks or airports.

In some embodiments, it is assumed that we are performing wireless signal transmission testing in a city, with the goal of optimizing the signal strength of the receiving module to improve overall communication performance. We will use a set of path loss models to evaluate the propagation of signals in urban environments.

During the test, the environmental characteristic data collected by us include:

First characteristic data, namely the height, the position and the distribution of a plurality of high-rise buildings in the city.

And second characteristic data, namely weather information (such as cloudy days and rainy days) and time information (such as early peak, late peak and the like).

The receiving module receives signals from a plurality of transmitting nodes at a specific location. Assume that at a certain test instant, the received signal strength is as follows:

the received signal strength of the transmitting node a is 70 dBm (the ideal signal strength should be 85 dBm).

The received signal strength of the transmitting node B is 90 dBm (the ideal signal strength should be 80 dBm).

The preset values are set such that the first preset value is 75 dBm and the second preset value is 85 dBm.

For the first characteristic data (influence of the high-rise building), the received signal strength of the transmitting node a is 70 dBm, which is smaller than the first preset value (75 dBm). Therefore, the first characteristic data corresponds to a signal strength lower than the preset value.

For the second characteristic data (weather effect), the received signal strength of the transmitting node B is 90 dBm, which is greater than the second preset value (85 dBm). Therefore, the second characteristic data corresponds to a signal strength higher than the preset value.

And according to the judgment result, carrying out parameter adjustment on the path loss model:

for the transmitting node A, the signal strength is lower than a first preset value, so that the position is greatly influenced by high-rise shielding. The signal attenuation coefficient can be increased, and related parameters in the path loss model, such as the height or the number of high-rise buildings considered in the model, can be adjusted, so that the attenuation condition of the signal in the high-rise building environment can be reflected better.

For a transmitting node B, this indicates that the signal propagation conditions in this region are good, since its signal strength is higher than a second preset value, which may be at risk of over-prediction. Thus, we can suitably reduce the signal attenuation parameters so that the path loss model can more accurately represent the signal propagation conditions of this environment.

After parameter adjustment, the method continues monitoring and testing, and the updated path loss model is applied in subsequent data analysis to ensure more accurate signal strength evaluation in the environment and provide support for subsequent communication and positioning.

Optionally, when the corresponding first characteristic data at the transmitting node is smaller than a first preset value and the second characteristic data is larger than a second preset value, the path loss model is adjusted based on the two characteristic data, and the influence of the two characteristic data is required to be comprehensively considered for adjusting the path loss model so as to accurately reflect the change of the environment and optimize the positioning accuracy. By dynamically adjusting the path loss model, the influence of environmental changes on signal propagation can be effectively influenced. Such flexibility not only improves the positioning accuracy of the system, but also enhances the adaptability of the system.

And determining a position of the corresponding receiving module based on the weight data of each transmitting node, the corresponding position data and the estimated distance.

Specifically, the system obtains real-time signal strength information (analysis data) via a receiving module, which is typically from a plurality of transmitting nodes (e.g., base stations, routers, etc.). And obtaining the estimated distance from the transmitting node to the receiving module based on the preliminary measurement and calculation of the signal intensity data.

And calculating the corresponding signal to noise ratio by analyzing the received signal strength data. This data is a key indicator for evaluating signal quality, a high signal-to-noise ratio generally implies better signal quality and higher transmission reliability.

And then the system calculates weight data for each transmitting node through the signal strength data and the signal-to-noise ratio data. The weight may be calculated based on the principle that the stronger the signal strength and the higher the signal-to-noise ratio, the greater the weight of the transmitting node in the location estimation. Otherwise, the weight is reduced. The weight mechanism can reflect the influence degree of each sending node on position determination, and improves the accuracy of evaluation.

According to the weight data and the estimated distance of each transmitting node, calculating the actual position of the receiving module by using methods such as weighted average and the like:

Examples of the position calculation formula are:

;

Wherein, P _RX is the position of the receiving module, P _i is the position data of the ith transmitting node, and W _i is the weight data of the ith transmitting node.

By introducing signal-to-noise ratios and weights, the system is able to better evaluate the contribution of each transmitting node to the position estimate, especially in the presence of interference or signal attenuation. And the signal-to-noise ratio and the signal strength data are used for weighting, so that the position estimation precision can be effectively improved, and the position deviation caused by interference or signal attenuation is reduced. The weight-based data processing method reduces the influence of a remote node or a node with poor signal quality on the final result, and improves the stability and reliability of position estimation. Meanwhile, the signal strength and the signal to noise ratio are real-time available data, and the scheme can provide the position of the receiving module in real time through rapid calculation, which is very critical for real-time application (such as navigation, real-time monitoring and the like). By using a combination of signal-to-noise ratio and signal strength, wireless communication resources can be managed and optimized more effectively, ensuring that optimal positioning services can be obtained within a limited frequency band. Meanwhile, by a specific weight calculation method, the system can simplify the complexity of multi-node calculation, reduce the consumption of calculation resources and improve the performance of the whole system. And the scheme has strong adaptability to environmental changes. In the environment where the signal is weak or interference exists, the calculation of the signal to noise ratio can automatically adjust the weight of the transmitting node, so that the position estimation is more robust.

Namely, the scheme constructs a weight-based data processing method to determine the position of the receiving module by analyzing the signal strength data, the signal-to-noise ratio data and the corresponding estimated distance. The method not only improves the positioning precision and robustness, but also enhances the adaptability of the system in complex environments, thereby providing powerful support for practical application.

In one embodiment, a single code channel based location identification system is described, which is primarily used for indoor location services, and is suitable for use in large malls, exhibitions, or other complex environments. The system consists of a transmitting module, a single code channel module, a receiving module, a calculating module and an environment sensing module.

The transmitting module comprises four transmitting nodes (node A, node B, node C and node D), wherein every two adjacent nodes form a group of transmitting node groups, namely a transmitting node group 1, namely the node A and the node B, and a transmitting node group 2, namely the node C and the node D. Each transmitting node transmits signal data according to a preset time interval (such as once per second), and the signal data adopts specific preset waveform parameters (such as amplitude is 1V, frequency is 2.4GHz, and waveform signal duration is 100 ms). And transmitting the node pair 1, namely the node A and the node C, and transmitting the node pair 1, namely the node B and the node D.

And a receiving module configured at a specific position in the room and capable of receiving the signal data from the transmitting node pair. And separating the signal data to obtain two temporary signal data and two signal intensity data, carrying out matching identification on the temporary signal data through preset waveforms, determining corresponding transmitting nodes, and analyzing to obtain the signal intensity data.

And the calculating module is used for calculating the estimated distance from the receiving module to each sending node based on the signal intensity data and the path loss model obtained by the receiving module. And combining the signal strength and the signal-to-noise ratio data to determine the final position of the receiving module.

And the environment sensing module is used for collecting environment change data through sensors (such as temperature and humidity sensors) and extracting characteristics to obtain environment characteristic data. And carrying out parameter adjustment on the path loss model according to the environmental characteristic data so as to improve the accuracy of the position estimation.

The implementation step is that each sending node sends signal data according to preset time intervals. And the node A transmits the signal data, then the node C, the node B and the node D sequentially transmit the signal data, and the initial signal data of each transmitting node in the transmitting node pair are fused through the single code channel module to obtain the signal data corresponding to the transmitting node pair.

The receiving module receives the signal data from each sending node pair and identifies the source of the signal according to the preset waveform parameters. By analyzing the signal data, the receiving module obtains corresponding signal strength data (e.g., the signal strength received from node a is 55 dBm).

The calculation module calculates a signal-to-noise ratio (e.g., a signal-to-noise ratio of 15 dB) based on the received signal strength data. Weight data for each transmitting node is calculated based on signal strength and signal-to-noise ratio (e.g., node a weights 0.6, node B weights 0.3, and node C weights 0.1). And then using the path loss model, calculating an estimated distance between the receiving module and each transmitting node according to the signal strength data (for example, the estimated distance of the node A is 10 meters, the estimated distance of the node B is 15 meters, and the estimated distance of the node C is 20 meters).

Finally, calculating the position of the receiving module according to the weight and the estimated distance of each transmitting node by a weighted average method:

;

Meanwhile, the environment sensing module collects environment change data (such as signal attenuation caused by temperature change) and extracts characteristic data. Parameters of the path loss model are adjusted based on the characteristic data (e.g., attenuation coefficients in the model are adjusted when the signal strength is less than 60 dBm).

According to the embodiment, the position estimation accuracy of the receiving module can be effectively improved through comprehensive calculation of the signal strength, the signal-to-noise ratio and the weight. Meanwhile, due to the addition of the environment sensing module, the system can adjust the path loss model in real time, adapt to environment changes and ensure the positioning reliability.

The use of the single code channel module simplifies coordination of signal transmission, reduces signal interference and improves the overall efficiency of the system.

The embodiment shows a single code channel-based position identification system, which can realize high-precision positioning service in a complex environment through signal transmission and reception of multiple transmission nodes, analysis of signal strength and signal to noise ratio, and real-time monitoring and model adjustment of environment data, and has wide application potential.

As shown in fig. 3, the position identifying method based on a single code channel provided by the embodiment of the invention is applied to the position identifying system based on the single code channel, and the position identifying method based on the single code channel includes:

step S100, each transmitting node is controlled to transmit initial signal data according to a preset time interval;

Step 200, fusing initial signal data of each sending node in a sending node pair to obtain signal data corresponding to the sending node pair, wherein the sending node pair is formed by each sending node in a target sending node group and one sending node in a non-target sending node group, and the target sending node group is formed by two adjacent sending nodes;

step S300, carrying out identification analysis on each signal data to obtain analysis data;

Step S400, based on a path loss model, obtaining a corresponding estimated distance according to each piece of analysis data, and determining the position of the corresponding receiving module according to each piece of analysis data and the corresponding estimated distance.

Specifically, each transmitting node (e.g., node a, node B, node C, node D) is controlled to transmit signal data through a single-code-channel module according to a preset time interval (e.g., transmission once per second). Each transmitting node adopts specific preset waveform parameters (such as amplitude, frequency and waveform duration) when transmitting signals, so that the identifiability of the signals at the receiving module is ensured. Fusing initial signal data of each sending node in a sending node pair to obtain signal data corresponding to the sending node pair;

These signal data are subjected to recognition analysis. The identification process includes comparing the waveform, amplitude, etc. characteristics of the signal to determine the source of the signal, the transmitting node.

An estimated distance between the receiving module and each transmitting node is calculated using the received signal strength data based on the path loss model. The path loss model takes into account the attenuation characteristics of the signal during propagation, typically including free space loss, the impact of environmental factors (e.g., obstructions, interference, etc.) on the signal.

Based on the analysis data and the corresponding estimated distance of each transmitting node, a weighting algorithm (e.g., weighted average) is used to calculate the actual position of the receiving module. The specific calculation process can be combined with the signal-to-noise ratio data to allocate weight to each transmitting node, so that the accuracy of position estimation is improved.

By integrating the signal strength and the path loss model, the distance between the receiving module and the transmitting node can be estimated more accurately, and the positioning accuracy is further improved. And the method can adapt to different environmental changes. By dynamic adjustment of the path loss model, the system can maintain good positioning performance in different indoor environments. The signal transmission mechanism with preset time interval ensures that the system can acquire the latest signal data in real time, quickly respond to the environment change and provide instant positioning information. The method is suitable for various application scenes, such as indoor navigation, asset tracking, intelligent home and the like, and has strong market potential.

The position identification method based on the single code channel of the embodiment forms an efficient and accurate positioning mechanism by controlling the signal transmission of the transmitting node, the identification and analysis of the signal data, the calculation of the estimated distance and the determination of the position of the receiving module. The implementation of the method can obviously improve the application effect of the position identification system in a complex environment, and provide more reliable technical support for the related field.

As shown in fig. 4, an electronic device 400 according to an embodiment of the present invention includes a memory 410 and a processor 420, where the memory 410 is configured to store a computer program, and the processor 420 is configured to implement the single-track position measurement method described above when executing the computer program.

Alternatively stated, an electronic device 400 comprises a memory 410 and a processor 420 coupled to the memory 410, the memory 410 being configured to store a computer program, the processor 420 being configured to, when executing the computer program, perform the following:

And determining the position of the corresponding receiving module according to each analysis data and the corresponding estimated distance.

The embodiment of the invention provides a computer readable storage medium, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the single code channel position measurement method is realized.

Alternatively, a non-transitory computer readable storage medium having a computer program stored thereon, which when executed by a processor, causes the processor to:

An electronic device 400 that may be a server or a client of the present invention will now be described as an example of a hardware device that may be applied to aspects of the present invention. Electronic device 400 is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic device 400 may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

The electronic device 400 includes a computing unit that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) or a computer program loaded from a storage unit into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the device may also be stored. The computing unit, ROM and RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.

Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ReadOnly Memory, ROM) or a random-access memory (Random Access Memory, RAM), etc. In the present application, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present application. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

Although the invention is disclosed above, the scope of the invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications will fall within the scope of the invention.

Claims

1. The position identification system based on the single code channel is characterized by comprising a transmitting module, a single code channel module, at least one receiving module and a calculating module;

The transmitting module comprises at least four transmitting nodes, wherein each transmitting node transmits initial signal data to the receiving module through the single code channel module according to a preset time interval, two adjacent transmitting nodes form a group of transmitting node groups, and each transmitting node in a target transmitting node group and one transmitting node in a non-target transmitting node group form a transmitting node pair respectively;

2. The single-track based location identification system of claim 1, wherein the initial signal data transmitted by each of the transmitting nodes is generated according to corresponding preset waveform parameters, the preset waveform parameters corresponding to each of the transmitting nodes being different, the preset waveform parameters including one or more of amplitude, frequency and duration of a waveform signal.

3. The single-track based location identification system of claim 2, wherein the signal data comprises signal strength data of initial signal data of two of the transmitting nodes in the pair of transmitting nodes, the receiving module being specifically configured to:

4. The single-track based location recognition system of claim 3, further comprising an environmental sensing module;

5. The single code channel based location identification system of claim 4, wherein the parameter adjustment of the path loss model based on the environmental characteristic data comprises:

6. The single code channel based location identification system of claim 4, wherein the parameter adjustment of the path loss model based on the environmental characteristic data comprises:

7. The single code channel based location identification system of claim 3 wherein said analysis data further comprises location data for each of said transmitting nodes in said pair of transmitting nodes, said determining a location for a corresponding said receiving module based on each of said analysis data and a corresponding said estimated distance further comprising:

8. A single code channel based position identification method, applied to the single code channel based position identification system according to any one of claims 1 to 7, comprising:

9. An electronic device comprising a memory and a processor;

The memory is used for storing a computer program;

The processor is configured to implement the single code channel based location identification method of claim 8 when executing the computer program.

10. A computer readable storage medium, wherein a computer program is stored on the storage medium, which when executed by a processor, implements the single code channel based position identification method of claim 8.