CN112180400A - Positioning device, method and system - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
- G01S19/07—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing data for correcting measured positioning data, e.g. DGPS [differential GPS] or ionosphere corrections
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/22—Multipath-related issues
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/23—Testing, monitoring, correcting or calibrating of receiver elements
- G01S19/235—Calibration of receiver components
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Abstract
本发明涉及定位装置、方法以及系统。根据本发明的定位装置、方法以及系统基于候选位置样本点，通过分布候选位置样本点集，并重新分布候选位置点，再进行迭代运算，得到定位结果，为现代城市峡谷内GNSS定位导航提供精度更高而且响应速度更快的解决方案。
The present invention relates to positioning devices, methods and systems. According to the positioning device, method and system of the present invention, based on the candidate position sample points, by distributing the candidate position sample point set, redistributing the candidate position points, and then performing an iterative operation to obtain a positioning result, which provides accuracy for GNSS positioning and navigation in modern urban canyons Higher and more responsive solutions.
Description
Technical Field
The present invention relates to the field of positioning Navigation, and in particular, to a device, method and System for improving the accuracy and response speed of Global Navigation Satellite System (GNSS) positioning Navigation.
Background
There may be a variety of reasons for errors and delays in GNSS based navigation or positioning. For example, pseudoranges derived from observation functions inevitably have errors due to surface roughness, ionospheric irregularities, inter-cloud differences, etc. In an urban canyon environment, errors accumulate due to multipath effects. As cities become more densely populated with higher buildings, there is an increasing need for improved positioning devices and methods.
Disclosure of Invention
Therefore, it is necessary to provide a device, a method and a system for improving positioning accuracy based on GNSS to solve the problems of the existing GNSS positioning scheme that the GNSS navigation system has the defect effects of inaccurate navigation and slow navigation due to the reflection and refraction multipath interference effects and unavoidable observation errors in urban canyon scenes.
The invention provides a device, a method and a system for improving GNSS positioning accuracy and response speed.
According to an aspect of the present invention, there is provided a positioning apparatus including a storage unit for storing a first time position of an object; and a processing unit connected to the storage unit, the processing unit configured to: generating a plurality of candidate position sample points according to the first time position of the target and the characteristic vector of the first time position associated with the first time position, wherein the candidate position sample points are distributed in a sample space; according to the similarity degree between the feature vector of the candidate position sample point and the feature vector of the first time position, redistributing the candidate position sample points to obtain redistributed candidate position sample points; and obtaining the position of the target after the first time instant from the redistributed candidate position sample points.
The storage unit can also store a motion equation based on the target characteristic; the processing unit is further configured to: the plurality of candidate location sample points are generated by applying equations of motion. The equation of motion includes a random variable. The feature vector of the first time position comprises a pseudo range obtained by a GNSS observation equation; and the first time location comprises a location determined from the pseudoranges. The redistribution further includes: solving a likelihood function according to the similarity between the feature vector of the candidate position sample point and the feature vector of the first time position to obtain the likelihood degree of each candidate position sample point; and setting more redistributed candidate position sample points near the candidate position with higher likelihood. The redistribution further includes: solving a likelihood function according to the similarity between the feature vector of the candidate position sample point and the feature vector of the first time position to obtain the likelihood degree of each candidate position sample point; and the more densely the redistributed candidate location sample points are placed near the candidate location with the higher likelihood. The position after the first time instant is derived from the weighted average position of the redistributed candidate position sample points. The weighted weight is obtained by the likelihood function transformation and normalization.
According to an aspect of the present invention, there is provided a GNSS signal based system, including a receiver for receiving GNSS signals; and a positioning device as described above coupled to the receiver.
According to an aspect of the present invention, there is provided a positioning method, including: acquiring a first time position of a target; generating a plurality of candidate position sample points according to the first time position of the target and the characteristic vector of the first time position associated with the first time position, wherein the candidate position sample points are distributed in a sample space; according to the similarity degree between the feature vector of the candidate position sample point and the feature vector of the first time position, redistributing the candidate position sample points to obtain redistributed candidate position sample points; and obtaining the position of the target after the first time instant from the redistributed candidate position sample points.
According to an aspect of the invention, the method further comprises generating a plurality of candidate location sample points by applying a motion equation, wherein the motion equation is based on the target feature.
The equation of motion includes a random variable. The feature vector of the first time position comprises a pseudo range obtained by a GNSS observation equation; and the first time location comprises a location determined from the pseudoranges. The redistribution further includes: solving a likelihood function according to the similarity between the feature vector of the candidate position sample point and the feature vector of the first time position to obtain the likelihood degree of each candidate position sample point; and setting more redistributed candidate position sample points near the candidate position with higher likelihood. The redistribution further includes: solving a likelihood function according to the similarity between the feature vector of the candidate position sample point and the feature vector of the first time position to obtain the likelihood degree of each candidate position sample point; and the more densely the redistributed candidate location sample points are placed near the candidate location with the higher likelihood. The redistribution further includes: setting a first batch of candidate position sample points near the candidate position with higher likelihood degree; and placing a second plurality of candidate location sample points in the vicinity of the less likely candidate locations, wherein the first plurality of candidate location sample points is more numerous than the second plurality of candidate location sample points to form the first plurality of candidate location sample points with the second plurality of candidate location sample points into redistributed candidate location sample points. The redistribution further includes: and shifting from the candidate position sample point to the candidate position with higher likelihood degree.
The position after the first time instant is derived from the weighted average position of the redistributed candidate position sample points. The weighted weight is obtained by the likelihood function transformation and normalization.
According to an aspect of the invention, there is provided a method of improved GNSS positioning, the method comprising: acquiring a first position of the target, wherein the first position comprises a position set in a three-dimensional space according to the pseudo range; generating a plurality of candidate position points of a first generation candidate position by taking the first position as a center; and iteratively generating a plurality of candidate location points for the next generation of candidate locations.
According to one aspect of the invention, an equation of motion of the target is established and an equation of observation of the target is obtained, releasing the candidate location sample points probabilistically. And according to the GNSS satellite navigation result, combining with the target characteristics, applying a proper limiting condition, establishing a motion equation of the target in a program, and releasing the candidate position sample points according to probability. And determining a first generation position origin of the user according to the GNSS satellite navigation result at the starting moment. Based on probability theory, a sample space is defined as a mapping of the user usage scenario. Defining a distribution function according to actual needs and a use scene of a user, and randomly generating a first generation of equal-weight candidate position sample point set distributed by taking a position origin as a center according to the probability density of the function. And acquiring a feature vector of a first generation position origin, and solving the feature vector of each sample point in the first generation candidate position set. And solving a likelihood function according to the similarity degree of the feature vectors of each sample point and the original point, transforming and normalizing the likelihood function to obtain the weight of each sample point, and solving the weighted average position of each sample point in the candidate position set to obtain a first generation target position result.
According to an aspect of the invention, redistributing the candidate location points comprises: and redistributing the first generation candidate position points according to the weight values of the first generation sample points. More sample points are placed near locations with a high degree of likelihood, and correspondingly less sample points are placed at locations with a low degree of likelihood. A first generation redistribution candidate location sample point set is obtained.
According to an aspect of the invention, establishing a new time instant candidate position sample point equation according to the motion equation comprises: and according to the motion equation, the initial moment position origin and all the first generation redistribution candidate position sample points move to a new moment position to obtain a second generation position origin and a second generation candidate position sample point set.
According to an aspect of the invention, the output of the result of the iterative operation comprises: and according to the operation used for the first generation of candidate position sample points, acquiring a second generation of position origin and a feature vector of a second generation of candidate position sample point set, calculating a likelihood function, and calculating and redistributing a target position result. Thereby obtaining the offspring candidate position sample points and the offspring target position result.
Compared with the prior art, the invention has the following excellent effects:
the invention provides a GNSS positioning accuracy improving device, a GNSS positioning accuracy improving method and a GNSS positioning accuracy improving system. Firstly, according to a GNSS satellite navigation result, combining with target characteristics, applying appropriate limiting conditions, establishing a motion equation of a target, obtaining an observation equation of the target, releasing candidate position sample points according to probability, calculating a characteristic vector, a likelihood function and a normalized weight of the candidate position sample points. And then redistributing the candidate position sample points according to the weight values. And generating a new time candidate position sample point equation according to the motion equation. And finally, carrying out iterative operation to obtain a result. The method can make up for the defects of high positioning error, navigation slowness and the like caused by the multipath effect which cannot be avoided by the existing navigation and navigation technology in the urban canyon environment, reduce the unavoidable observation and the motion equation error and improve the precision of the navigation system. An effective solution is provided for the improvement of the GNSS navigation system in the modern urban canyon.
Drawings
In the following description, specific implementations are described as examples of the invention with reference to the accompanying drawings. It is to be understood that other embodiments may be utilized and structural changes may be made without departing from the scope of the present invention. In the drawings, like reference numbers indicate identical or functionally similar elements throughout the separate views.
FIG. 1 is a schematic diagram of a positioning apparatus and a positioning system according to an embodiment of the invention;
FIG. 2 is a schematic flow chart diagram of a positioning method according to an embodiment of the invention;
FIG. 3 is a schematic flow chart diagram of a positioning method according to yet another embodiment of the present invention;
FIG. 4 is a schematic flow chart diagram of a positioning method according to an embodiment of the invention;
FIG. 5 is a schematic flow chart diagram of redistributing candidate location points according to an embodiment of the present invention;
FIG. 6 is a data plot showing an output raw trajectory of a GNSS satellite and a positioning trajectory according to an embodiment of the present invention; and
FIG. 7 is a data graph illustrating an error between a GNSS satellite output raw trajectory and a true result and an error between a GNSS satellite output raw trajectory and a true result according to an embodiment of the present invention.
Detailed Description
FIG. 1 illustrates a GNSS signal based system 100 according to an embodiment of the present invention. As shown in FIG. 1, the system 100 includes a positioning device 110 and a receiver 140 coupled to the positioning device 110. By way of example and not limitation, the positioning device 110 includes a storage unit 130 and a processing unit 120. It should be understood that the system may also directly include the storage unit 130, the processing unit 120, and the receiver 140. Alternatively, the positioning device 110 may include the processing unit 120, and the positioning device 110 is connected to an external storage unit 130 and/or receiver 140.
The receiver 140 is configured to receive GNSS signals from satellites. The receiver 20 may be configured to receive the pseudoranges and determine a first position of the receiver 20 therefrom.
When the at least one GNSS signal received by the receiver 140 is a signal reflected from a building or other facility, the pseudorange computed by the receiver 140 will include some degree of error. For example, an automobile may be configured with a navigation device that includes a receiver 140. The automobile may be traveling on a road between tall buildings such that at least one GNSS signal received by the receiver 140 has been reflected by the building before the GNSS signal is received by the receiver 140. Therefore, the pseudoranges measured based on the reflected GNSS signals need to be corrected to correct the navigation of the car. It will be appreciated that this problem will be more challenging when the receiver is in a mobile device carried by a person.
Some positioning schemes propose correcting pseudorange measurements. It will be seen from the following description that the approach taken by the present application to address this problem is different from the general approach.
For purposes of example, an embodiment is described with reference to a user (person) moving in a city. In this embodiment, the user carries a mobile device configured with the processing unit 120.
The processing unit 120 is further coupled to a storage unit 130. The storage unit may store data describing the physical environment. Examples of physical environments include maps, floor plans of buildings, and so forth. The storage unit may be configured to store the sample space. The sample space may be a map of the physical environment. The sample space may be a plurality of possible locations of the user in the physical environment. The sample space may include a plurality of possible locations of the user in the physical environment at any time. The sample space may include all possible locations in the physical environment where any user may be located. For example, when the physical environment includes a room on a high floor, the sample space may include all locations in the room and the sample space may simultaneously exclude locations in the space outside the room.
The various locations of the user may be described by vectors. The various positions of the user may be described by a vector that includes the direction in which the user is facing. The various locations of the user may be described in terms of coordinates. The various positions of the user can be described by displacements relative to reference points in the sample space.
In this example, a first location of the user at a first time is known. The first position may be known from one or more pseudoranges computed from a receiver 140 coupled to the processing unit 120, wherein the receiver is configured to compute the one or more pseudoranges based on GNSS signals. The first position may be provided to the processing unit by the receiver. One or more pseudoranges used to compute the first position may be provided by the receiver to the processing unit. For purposes of example, the first location may be characterized in terms of a first pseudorange. The first pseudorange may be taken as an example of a vector characteristic of the first position in sample space.
The processing unit 120 is configured to convert the known position into a plurality of candidate position sample points. All candidate position sample points are at various displacements from the known position and all candidate position sample points are given the same weight. For all candidate location sample points associated with one known location, all candidate location sample points are assigned the same probability value between 0 and 1. In other words, all the candidate position sample points are assigned a probability of [0,1 ]. For ease of reference, the candidate location sample points for a location are collectively referred to as a set of candidate location sample points.
Continuing this example, using the first location of the user, the processing unit is configured to provide a set of first candidate location sample points. The plurality of first candidate location sample points form a set of equally probable locations associated with the first location. The processing unit is configured to distribute the first candidate location sample points with respect to the first location. The first candidate location sample points may be scattered around the first location. The processing unit may be configured to randomly distribute a plurality of first candidate location sample points in the vicinity of the first location. The processing unit may be configured to apply a distribution function to generate a plurality of first candidate location sample points. The first location may be located at a center of a distribution of the plurality of first candidate location sample points. The plurality of first candidate location sample points may be described as having equal weight with respect to the first location. Conventional positioning and navigation schemes treat the user as a certain physical entity that occupies a certain position in space. From a probabilistic point of view, the usage environment is a sample space, where the user occupies a certain position with a probability of 1 under the conventional scheme. Unlike the conventional scheme, in the present invention, a user is regarded as a set of candidate position sample points, each candidate position sample point occupies a spatial position with a probability a smaller than 1, and the sum of the weights of all candidate position sample points is 1. The method discretizes the physical position of the user, and can remarkably reduce the influence degree of sudden change among the direct signal, the reflected signal and the refracted signal on the sudden change of the output position result of the navigation program when the user passes through some special positions.
The processing unit is configured to consider the physical environment when distributing the first candidate location sample points. For example, the physical environment may have been mapped to the sample space such that the sample space includes only locations or locations where candidate location sample points may be located. Thus, if a forbidden space is defined in a physical environment, the sample space may be defined as follows: one of the location or candidate location sample points will not be defined or placed in the forbidden space. For example, the sample space may be defined as an exterior space that does not include a high-rise room of a building. Although in some cases, for example, where the user is a drone, the user may occupy such a location, in the present embodiment the user is a person, so the processing unit may be configured to exclude such a location (position).
The processing unit is configured to obtain a generation of feature vectors corresponding to candidate position sample points of the position at a certain time. In this example, the processing unit is configured to obtain a first generation of candidate position sample point feature vectors corresponding to the first candidate position sample point at a first time instant. In other words, for each of the plurality of first candidate position sample points, a corresponding first candidate position sample point feature vector is obtained.
For each first candidate position sample point, the processing unit is configured to compare the first candidate position sample point feature vector with the first position vector. As mentioned above, in this example, the first position vector is in this case the first pseudorange for the first position. The processing unit is configured to compare the first pseudorange to each candidate position sample point feature vector. A likelihood function may be derived based on the similarity of the first pseudorange to the feature vector of each candidate position sample point. The processing unit is configured to normalize the likelihood function to obtain a respective first weight for each first candidate position sample point.
The processing unit is configured to obtain a weighted average of the first candidate position sample points using the first weight.
The processing unit may be configured to provide the weighted average as the improved first location of the user. The processing unit may be configured to alternately apply the first weights to the respective first candidate position sample points and redistribute the first candidate position sample points accordingly. The redistribution may comprise defining more candidate position sample points in the vicinity of first candidate position sample points having a relatively higher first weight or greater likelihood and defining fewer candidate position sample points in the vicinity of first candidate position sample points having a relatively lower first weight or lesser likelihood. This redistribution results in a generation of redistributed candidate location sample points. A new generation of redistributed candidate location sample points is also found in the sample space.
The processing unit is configured to generate a next generation position using the user equation of motion. In this example, the user is a person who owns the mobile device. The user equation of motion may be provided in the mobile device, such as in a memory unit coupled to the processing unit. The user motion equations may be pre-encoded in firmware of the mobile device and provided to the processing unit. The user may select a user equation of motion. The user's equation of motion may be selected manually or may be selected automatically without manual intervention. The user equation of motion may be one selected from a number of possible user equations of motion.
Continuing with the present example, in the present embodiment, the user's equations of motion applied by the processing unit are based on random motion or brownian motion. The processing unit is configured to apply the user equation of motion to the first location and each redistributed first candidate location. The result is a second location and a corresponding plurality of second candidate locations. In other words, using a first position of the user at a first time and the user equation of motion, a second position of the user at a time after the first time is obtained. For brevity, a time after the first time may be referred to as a second time. In addition, for each first candidate position sample point of the user at the first time and the user motion equation, a corresponding second candidate position sample point is obtained. In other words, the positions of the candidate position sample points of the second generation (or new generation, or descendants) are obtained. The plurality of second candidate location sample points may be collectively referred to as a second set of candidate location sample points for a time interval after the first time instant.
The processing unit is configured to describe the second location with a second feature vector. The processing unit is configured to describe each second candidate position sample point with a respective second candidate position feature vector.
For each second candidate position sample point, the processing unit is configured to compare the second candidate position feature vector with the second position feature vector. The processing unit is configured to compare the second feature vector with each second candidate position sample point feature vector. And obtaining a likelihood function according to the similarity between the second feature vector and each second candidate position sample point feature vector. The processing unit is configured to normalize the likelihood function to obtain a respective second weight for each second candidate position sample point.
The processing unit is configured to obtain a weighted average of the second candidate locations using the second weight. The processing unit may be configured to provide the weighted average as the improved second location of the user. In this way, a new location of the user may be determined from the known location, wherein the user moves from the known location to the new location within a time interval.
According to an embodiment of the present invention, as shown in FIG. 2, a method 200 for locating an object includes, at step 210, obtaining a first temporal position of the object. The first moment position of the target may be stored in the storage unit 130 of the positioning device 110 as described in fig. 1. Alternatively, the first time position may be stored in a memory external to the positioning device 110. Alternatively, the first time instant position of the target may be obtained from the receiver 140.
In step 220, a plurality of candidate position sample points are generated according to the first time position of the target and the feature vector of the first time position, wherein the plurality of candidate position sample points are distributed in the sample space, and the feature vector of the first time position is associated with the first time position. By way of illustration and not limitation, the processing unit of the positioning device may be configured to convert the known position into a plurality of candidate position sample points.
In step 230, a plurality of candidate position sample points are redistributed according to the similarity between the feature vector of the candidate position sample point and the feature vector of the first time position to obtain redistributed candidate position sample points. For each first candidate position sample point, the processing unit may be configured to compare the first candidate position sample point feature vector with the first position vector. The processing unit may be configured to compare the first pseudorange to each candidate position sample point feature vector. A likelihood function may be derived based on the similarity of the first pseudorange to the feature vector of each candidate position sample point. The processing unit may be configured to normalize the likelihood function to obtain a respective first weight for each first candidate position sample point. The processing unit may be configured to obtain a weighted average of the first candidate position sample points using the first weight.
In step 240, the position of the target after the first time instant is obtained from the redistributed candidate position sample points. By way of illustration and not limitation, the processing unit may be configured to apply the user equation of motion to the first location and each redistributed first candidate location. The result is a second location and a corresponding plurality of second candidate locations. The processing unit may be configured to describe the second location with a second feature vector. The processing unit is configured to describe each second candidate position sample point with a respective second candidate position feature vector.
For each second candidate position sample point, the processing unit may be configured to compare the second candidate position feature vector with the second position feature vector. The processing unit may be configured to compare the second feature vector with each of the second candidate position sample point feature vectors. And obtaining a likelihood function according to the similarity between the second feature vector and each second candidate position sample point feature vector. The processing unit may be configured to normalize the likelihood function to obtain a respective second weight for each second candidate position sample point.
The processing unit may be configured to obtain a weighted average of the second candidate locations using the second weight. The processing unit may be configured to provide the weighted average as the improved second location of the user. In this way, a new location of the user may be determined from the known location, wherein the user moves from the known location to the new location within a time interval.
According to yet another embodiment of the present invention, as shown in FIG. 3, a method 300 for locating an object includes, at step 310, obtaining a first position of the object. The first position may include a position in three-dimensional space set based on the pseudoranges. At step 320, a plurality of candidate location points for a first generation of candidate locations are generated centered on the first location. At step 330, a plurality of candidate location points for a next generation of candidate locations are iteratively generated. The specific iterative process can be further appreciated from the detailed description below.
According to an embodiment of the invention, as shown in fig. 4, a candidate location sample point-based positioning method 400 includes, at step 410, distributing an initial set of candidate location sample points. Specifically, a distribution function is defined according to actual needs and a use scene of a user, and a first generation of equal-weight candidate position sample point set distributed by taking a position origin as a center is randomly generated according to the probability density of the function, wherein the position origin can refer to the coordinates of an initial position or a first position of the user at a first moment. The distribution function may be selected from gaussian distribution, exponential distribution, uniform distribution, etc. The present example assumes a gaussian distribution.
In step 420, candidate location sample point feature vectors are obtained. Specifically, a feature vector of a first generation position origin is obtained, and the feature vector of each sample point in a first generation candidate position set is solved. The feature vector may be selected from pseudorange, origin range difference, etc. In one embodiment, the selected feature vectors are pseudoranges. The pseudoranges to the position origin may be resolved directly from the ephemeris output by the GNSS navigation satellites. And substituting the pseudo range of each sample point in the candidate position set into the geographic parameter of each sample point by a program, and substituting the pseudo range into a pseudo range equation by combining ephemeris of the GNSS satellite.
At step 430, the candidate location sample point likelihood functions are solved. Specifically, the likelihood function is solved according to the degree of similarity of the feature vectors of each sample point and the origin. The likelihood function quantifies the difference between the sample point and the origin, and may select the original value difference of the feature vector, the original value ratio of the feature vector, the root mean square value difference of the feature vector, the root mean square value ratio of the feature vector, etc. In one embodiment, the likelihood function taken is the feature vector raw value difference. In this embodiment, the magnitude of the likelihood function value is inversely related to the magnitude of the candidate position sample point weight, and the likelihood function value is transformed by substituting it into the attenuation exponential function.
In step 440, candidate location sample point weights are calculated. Specifically, the values of all attenuation exponential functions are summed and normalized to obtain the weight of each sample point.
In step 450, the set of candidate location sample points is redistributed. Also, at step 460, the set of candidate position sample points may be generated into a new generation of a set of candidate position sample points based on the equation of motion. Steps 420 through 440 are then repeated for the new generation of candidate location sample point sets.
The method 400 proceeds from step 440 to step 470 when all or a specified sample point in the sample point set has been traversed through steps 410 to 460. At step 470, the redistributed candidate location sample point weighted average is solved. Specifically, the coordinates of each sample point are multiplied by the weight and then summed, and the weighted average position of each sample point in the candidate position set is solved, so as to obtain the position result 480 of the target.
It should be appreciated that the method 400 used in this embodiment may include establishing an equation of motion of the target and obtaining an equation of observation of the target using appropriate constraints based on GNSS satellite navigation results in combination with target features, releasing the candidate position sample points probabilistically, redistributing the candidate position points, establishing an equation of the candidate position sample points at a new time based on the equation of motion, and outputting results of iterative operations, thereby obtaining improved GNSS positioning accuracy and response speed.
In addition, according to the characteristics of the user, the characteristics can be divided into a speed characteristic, a steering characteristic, an avoidance characteristic and the like, and a motion equation matched with the use scene of the user as much as possible is established in the program. The motion equation can be a uniform motion equation, a uniform acceleration motion equation, a Brownian motion equation and a static equation. In one embodiment, a superposition equation of uniform motion and brownian motion is used. And determining a first generation position origin of the user according to the GNSS satellite measuring position result at the starting moment. When a user starts to use the GNSS satellite for navigation, the receiver can be synchronized with the GNSS satellite through three modes of cold start, warm start and hot start according to different capabilities of the ground-based auxiliary system. After synchronization, the receiver outputs the user's initial position.
Based on probability theory, a sample space is defined as a mapping of the usage scenario. And calling the 3D building model file of the city, geometrically quantizing the 3D map of the city, and acquiring the characteristics of the 3D map. The characteristics comprise the outline boundary of the building, the boundary of a sidewalk, the boundary of a driveway, the distribution of trees, the distribution of tunnels, building materials, electromagnetic parameters and the like.
FIG. 5 is a flow diagram illustrating a method 500 for redistributing candidate location points in accordance with an embodiment of the present invention, which illustrates a specific iterative process, but is not limited thereto. And redistributing the first generation candidate position points according to the weight values of the first generation sample points. The redistribution method includes fixed threshold redistribution and random variable threshold redistribution. If fixed threshold distribution is adopted, the weights of the sample points at the candidate positions may be concentrated on a few points through multi-generation operation, and the weights of the sample points at other candidate positions approach 0, so that the practical value is lost. In order to avoid weight degradation, a weight reassignment method is introduced for re-weighting. The random variable threshold distribution can not generate the condition of weight degradation. In one embodiment, a random variable threshold redistribution is undertaken. The redistribution method is to traverse each sample point of the first generation sample point set, when the program evaluates a certain sample point, define a random number uniformly distributed between [0,1], accumulate the weights of other position sample points, if the first n items of the weights of the position sample points are not less than the random number, and the first (n-1) items are less than the random number, redistribute the position sample point to the position of the n item. And when each position sample point is traversed, rearranging the positions of the first generation candidate position points according to the weight value. Thereby, a first generation redistribution candidate location sample point set is obtained.
And then, establishing a new time candidate position sample point equation according to the motion equation. Specifically, the initial time position origin and all the first generation redistribution candidate position sample points are substituted into a preset motion equation, all the points move to the new time position, and a second generation position origin and a second generation candidate position sample point set are obtained.
And then, carrying out iterative operation to obtain a result. Specifically, according to the operation used for the first generation of candidate position sample points, the feature vectors of the second generation of position origin and the second generation of candidate position sample point set are obtained, the likelihood function is calculated, and the target position result is calculated and redistributed. And obtaining the offspring candidate position sample points and the offspring target position result.
Alternatively, according to fig. 5, each sample point of the first-generation sample point set is traversed, and in step 510, a candidate position sample point number i to be evaluated is determined, where i is an integer and is greater than or equal to 0. In step 520, a uniformly distributed random variable k (i) among [0,1] is defined. The random variable k (i) does not include a 0 value or a 1 value. Next, in step 530, sum the top n terms of the weight values of the sample points, i.e., s (j) ═ a (j) + s (j-1), where j is an integer and j is greater than or equal to 1. In step 540, the process proceeds to step 550 or step 560 according to the result of the determination condition. For example, if s (j) is less than k (i), proceed to step 550, otherwise proceed to step 560. For another example, it is determined whether the first n items of the weight values of the candidate sample points and S (j) are not random variables k (i), and the sum S (j-1) of the first (n-1) items of the weight values of the sample points is smaller than the random variables k (i). If S (j) ≧ k (i) and S (j-1) < k (i), proceed to step 550, otherwise proceed to step 560. In step 550, the sample point i is redistributed to the location of the candidate location sample point j. In step 560, a self-increment operation is performed on j, i.e., j equals j + 1. After step 560, flow jumps back to step 530. After step 550, at step 570, a self-increment operation is performed on i, i.e., i ═ i + 1. After step 570, flow jumps back to step 510, thus traversing each sample point of the first generation sample point set. And when each sample point is traversed, rearranging the positions of the first generation candidate position points according to the weight value. A first generation redistribution candidate location sample point set is obtained.
FIG. 6 is a data plot showing an example of a GNSS satellite outputting a raw trajectory 630 and an improved trajectory 620 using the present invention, respectively, in accordance with an embodiment of the present invention. As can be seen from fig. 6, the output trajectory obtained by the positioning method, apparatus and system according to the present invention is closer to the actual trajectory 610 of the target motion than the observed trajectory of the satellite. Therefore, the positioning method, the positioning device and the positioning system improve the precision of the navigation system.
FIG. 7 is a data graph illustrating an error 710 between a GNSS satellite output raw trajectory and a true result and an error 720 between a resulting trajectory and a true result, respectively, according to an embodiment of the present invention. As shown in fig. 7, the positioning error of the positioning method, apparatus and system according to the embodiment of the present invention is smaller than the error between the original GNSS satellite output trajectory and the true result. Obviously, the positioning method, the positioning device and the positioning system improve the accuracy of a navigation system. It can also be appreciated from fig. 7 that generally the longer the observation time, the greater the observation error. According to the embodiment of the invention, the positioning error is relatively stable compared with the observation track.
The method overcomes the defects of high positioning error, navigation slowness and the like caused by the multipath effect which cannot be avoided by the existing navigation and navigation technology in the urban canyon environment, reduces the unavoidable observation and motion equation error, and improves the precision of the navigation system. An effective solution is provided for the improvement of the GNSS navigation system in the modern urban canyon.
For the sake of brevity, all possible substitutions and/or combinations of features in the above-described embodiments are not described in detail, but should be construed to cover all embodiments falling within the scope of the present disclosure unless otherwise indicated.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be understood that various modifications, substitutions, additions, deletions, and the like may be made to the various embodiments and features therein without departing from the spirit and principles of the application, and without departing from the scope of the claims.
Claims (18)
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