CN108111440A - A kind of subchannel estimation distance measuring method of OFDM - Google Patents

Abstract

The invention proposes an OFDM sub-channel estimation and ranging method. The ranging method is divided into a parameter estimation stage and a distance estimation stage. In the parameter estimation phase, first randomly select a calibration point in the ranging area, and measure the distance from the calibration point to the access point (Access Point, AP); then the AP extracts the sub-channel state information from the data from the calibration point (Channel Information State, CSI), filter denoising and outlier detection at the same time; finally combine the distance from the calibration point to the AP and the sub-channel CSI to obtain the sub-channel signal propagation model. In the distance estimation stage, first filter and denoise the collected sub-channel CSI; then bring it into the sub-channel signal propagation model for distance estimation to obtain the sub-channel distance; as the distance from the target to the AP. Compared with the traditional ranging technology, the present invention does not need to perform CSI phase correction and compensation. In addition, the present invention has high ranging accuracy and is applicable to various ranging scenarios.

Description

An OFDM Subchannel Estimation and Ranging Method

technical field

The invention relates to the technical field of wireless positioning, in particular to an OFDM sub-channel estimation and ranging method.

Background technique

With the development of communication and information technology and the increasing demand for location-based services, location-based services have become one of the most popular service requirements at present. For outdoor positioning services, the current Global Positioning System (Global Positioning System, GPS) and Beidou system can provide relatively stable and high-precision location services. However, in indoor environments, signals from satellites will be blocked by buildings or the ground. , the positioning effect of GPS and Beidou system will be relatively poor. Moreover, many indoor location-based services require high positioning accuracy, such as finding the location of products in shopping malls, emergency rescue, and monitoring of special groups of people.

In the current research in the field of indoor positioning, distance-based ranging algorithms are mainly divided into three methods, ranging algorithms based on signal time of arrival (TOA) and ranging algorithms based on signal time difference of arrival (TDOA). Ranging algorithm and ranging algorithm based on energy information.

(1) Ranging algorithm based on signal arrival time

Based on the time of arrival technology, the obtained propagation time is multiplied by the propagation speed of the signal in the environment, so as to obtain the distance between the calibration point and the node to be measured. It is mainly divided into ranging based on signal one-way propagation time and ranging based on signal two-way propagation time. The ranging algorithm based on the one-way propagation time of the signal needs to know the precise moment when the signal is sent from the signal source at the receiving end to calculate the distance between the receiver and the signal source, so this method requires high clock synchronization. The distance measurement based on the round-trip propagation time of the signal adopts a round-trip time delay (Round-Trip Time, RTT) for the signal to propagate between the signal source and the receiver. In the round-trip delay measurement process, both the signal source and the receiver only need to record the time difference between sending out the signal and receiving the signal from the other party, without recording the precise moment when the signal is sent or received by itself.

(2) Ranging algorithm based on signal time difference of arrival

Time-of-arrival-based technology does not require very accurate clock synchronization at both ends of the transceiver, but the requirements for local clock errors are more stringent, that is, the receiver can accurately measure the arrival time of the signal and the receiver (reference point with known location) needs to Do strict time synchronization. Its main principle is that the calibration point sends a signal to the node to be tested, and the node to be tested reflects it back to the calibration point after receiving the signal, and uses the time difference between the signal sent by the calibration point and the signal received to calculate the distance between the node to be tested and the node to be measured. The distance between punctuation points.

(3) Distance estimation based on energy information

The distance estimation method based on energy information uses (Received Signal Strength, RSS) information, that is, the distance between the node to be measured and the calibration point is determined by measuring the strength value of the signal sent by the transmitting end at different positions in advance. Its main principle is that the signal will attenuate when it propagates in the wireless channel, and a mathematical model is established according to its attenuation law. A commonly used wireless channel attenuation model is the logarithmic normal attenuation model. RSS has no special restrictions on the clock and direction, and does not need to add additional hardware to realize the synchronization and angle measurement of the sending and receiving ends, and the cost is small. However, the parameters of the propagation model established by traditional distance estimation based on energy information are fixed. When the ranging environment changes, the established model cannot well reflect the change of signal energy, resulting in a decrease in ranging accuracy.

Contents of the invention

Based on the defects and deficiencies of the above-mentioned existing indoor positioning, the present invention provides an OFDM sub-channel estimation and ranging method. Compared with the traditional ranging technology, the present invention does not need to perform CSI phase correction and compensation. In addition, the present invention has high ranging accuracy and is applicable to various ranging scenarios.

The technical scheme adopted in the present invention is: a sub-channel estimation ranging method of OFDM, specifically comprising the following steps:

1) In the parameter estimation stage, a calibration point is randomly selected in the ranging environment where the target is located, and the distance from the selected calibration point to the Access Point (AP) is measured at the same time;

2) The target transmits data at the selected punctuation position, and the AP receives the arriving data, and extracts the sub-channel state information (Channel State Information, CSI) from it;

3) Filter the sub-channel state information obtained in step 2) using a Butterworth filter, suppress high-frequency noise, and obtain denoised sub-channel state information;

4) In combination with the sub-channel CSI obtained in the step 3) and the distance from the calibration point to the AP, the signal propagation model is parameterized, and finally the sub-channel signal propagation model is obtained;

5) In the distance estimation stage, the target can be at any position in the ranging area and send data, and at the same time, the AP receives the data sent by the target, and extracts the sub-channel CSI from the received data;

6) using a Butterworth filter to perform a denoising operation on the sub-channel CSI obtained in step 5), to obtain filtered sub-channel CSI;

7) The sub-channel CSI obtained based on the step 6) is brought into the sub-channel signal propagation model to obtain the sub-channel distance;

8) clustering the sub-channel distance obtained in the step 7), using the coordinates of the centroid as the distance from the target source to the AP;

2, based on a kind of OFDM described in claim 1 sub-channel estimation ranging method, it is characterized in that: use sub-channel estimation method and the outlier detection algorithm based on relative density to obtain sub-channel signal in the described step 4) Spread model. Specifically include:

At present, the logarithmic-normal model is used to represent the path attenuation of wireless signals in indoor environments, and its expression is:

P _R ＝P ₀ -10γlg(d) (1)

In the formula, P _R is the received signal energy value, P ₀ is the signal energy value at a distance of 1m from the target, d is the distance between the target to be measured and the AP, and γ is the path loss coefficient. The higher the degree of signal loss.

First, the AP receives the data sent by the target source at the selected punctuation point, extracts the sub-channel CSI from the received data, and at the same time filters and de-noises the obtained sub-channel CSI information, and forms the CSI matrix with the de-noised CSI information. The representation is:

In the formula, csi _n,m represents the csi value of the mth subchannel in the received nth data packet.

Then each sub-channel is brought into formula (1) as an independent channel, and the energy of the received signal in formula (1) is expressed as:

P _n,m =csi _n,m ×conj(csi _n,m ) (3)

In the formula, P _n,m is the signal energy value of the mth sub-channel in the received nth data packet, and conj(·) is the conjugate. The matrix of the obtained loss coefficient is expressed as:

where γ _n,m is the estimated signal loss coefficient on the mth subchannel in the nth data packet.

Then, the loss matrix is divided according to the number of sub-channels, that is, the number of columns of the matrix, and 30 sub-channel loss coefficient point groups are obtained, expressed as:

γ matrix＝[γ ₁ ,γ ₂ ,…,γ ₃₀ ] (5)

In the formula, γ _m is a vector composed of loss coefficients of the mth sub-channel.

An outlier detection algorithm based on relative density is used to detect anomalies for each sub-channel point group. First, specify the number p of neighbors. For the specified number of neighbors, calculate their density density(γ _n,m ,p) based on the nearest neighbors of each sub-channel loss coefficient point group, where γ _n,m is the mth The nth point of the sub-channel, from which the outlier score of each point group is calculated; then the average density of the neighbors of the point is calculated, and the average relative density of the point is calculated using the following formula:

In the formula, N(γ _n,m ,p) is the p-nearest neighbor point group of the target γ _n,m . This quantity indicates whether γ _n,m is in a denser or sparser neighborhood than its neighbors, and is taken as the outlier score for γ _n,m . Then the detected outliers are eliminated, and the remaining loss coefficient points are averaged at the same time, and the expression is:

In the formula, is the loss coefficient of the final m-th sub-channel, γ _n,m is the estimated signal loss coefficient on the m-th sub-channel in the n-th data packet, and k is the remaining loss coefficient points after removing outliers on each sub-channel . Finally, the loss coefficient of the signal propagation model of 30 sub-channels is obtained, expressed as:

Finally, the estimated sub-channel channel loss coefficient is brought into Equation (1), and finally the signal propagation model of 30 different path attenuation coefficients is obtained, expressed as:

In the formula, P _R,m is the signal energy received on the mth sub-channel, P _0,m is the signal energy value at the distance of 1m from the target on the mth sub-channel, is the path loss coefficient of the final mth subchannel.

The step 7) carries out target to the sub-channel distance estimation of AP, specifically includes:

According to the method as claimed in claim 2, a sub-channel signal propagation model is obtained. The AP receives multiple sets of data from the target, and obtains the CSI matrix shown in formula (2). The signal energy value of each subchannel is calculated separately, and its expression is:

In the formula, CSI _m is the energy value of the signal received by the mth sub-channel, csi _i,m is the CSI value received by the mth sub-channel in the i-th data packet, n is the number of received data packets, conj(·) To take the conjugate. Bring the signal energy values of the obtained 30 sub-channels into the sub-channel signal propagation model obtained in claim 2 to obtain 30 different distance values, expressed as:

d=[d ₁ ,d ₂ ,…,d ₃₀ ] (11)

Compared with the traditional ranging technology, the present invention does not need to perform CSI phase correction and compensation. In addition, the present invention has high ranging accuracy and is applicable to various ranging scenarios.

Description of drawings

Fig. 1 is a system flow diagram of the present invention

Detailed ways

Below in conjunction with accompanying drawing, the present invention is described in further detail:

The technical scheme adopted in the present invention is: a sub-channel estimation ranging method of OFDM, specifically comprising the following steps:

1) In the parameter estimation stage, a calibration point is randomly selected in the ranging environment where the target is located, and the distance from the selected calibration point to the Access Point (AP) is measured at the same time;

2) The target transmits data at the selected punctuation position, and the AP receives the arriving data, and extracts the sub-channel state information (Channel State Information, CSI) from it;

3) Filter the sub-channel state information obtained in step 2) using a Butterworth filter, suppress high-frequency noise, and obtain denoised sub-channel state information;

4) In combination with the sub-channel CSI obtained in the step 3) and the distance from the calibration point to the AP, the signal propagation model is parameterized, and finally the sub-channel signal propagation model is obtained;

5) In the distance estimation stage, the target can be at any position in the ranging area and send data, and at the same time, the AP receives the data sent by the target, and extracts the sub-channel CSI from the received data;

6) using a Butterworth filter to perform a denoising operation on the sub-channel CSI obtained in step 5), to obtain filtered sub-channel CSI;

7) The sub-channel CSI obtained based on the step 6) is brought into the sub-channel signal propagation model to obtain the sub-channel distance;

8) clustering the sub-channel distance obtained in the step 7), using the coordinates of the centroid as the distance from the target source to the AP;

2, based on a kind of OFDM described in claim 1 sub-channel estimation ranging method, it is characterized in that: use sub-channel estimation method and the outlier detection algorithm based on relative density to obtain sub-channel signal in the described step 4) Spread model. Specifically include:

At present, the logarithmic-normal model is used to represent the path attenuation of wireless signals in indoor environments, and its expression is:

P _R ＝P ₀ -10γlg(d) (1)

In the formula, P _R is the received signal energy value, P ₀ is the signal energy value at a distance of 1m from the target, d is the distance between the target to be measured and the AP, and γ is the path loss coefficient. The higher the degree of signal loss.

First, the AP receives the data sent by the target source at the selected punctuation point, extracts the sub-channel CSI from the received data, and at the same time filters and de-noises the obtained sub-channel CSI information, and forms the CSI matrix with the de-noised CSI information. The representation is:

In the formula, csi _n,m represents the csi value of the mth subchannel in the received nth data packet.

Then each sub-channel is brought into formula (1) as an independent channel, and the energy of the received signal in formula (1) is expressed as:

P _n,m =csi _n,m ×conj(csi _n,m ) (3)

In the formula, P _n,m is the signal energy value of the mth sub-channel in the received nth data packet, and conj(·) is the conjugate. The matrix of the obtained loss coefficient is expressed as:

where γ _n,m is the estimated signal loss coefficient on the mth subchannel in the nth data packet.

Then, the loss matrix is divided according to the number of sub-channels, that is, the number of columns of the matrix, and 30 sub-channel loss coefficient point groups are obtained, expressed as:

γ matrix＝[γ ₁ ,γ ₂ ,…,γ ₃₀ ] (5)

In the formula, γ _m is a vector composed of loss coefficients of the mth sub-channel.

An outlier detection algorithm based on relative density is used to detect anomalies for each sub-channel point group. First, specify the number p of neighbors. For the specified number of neighbors, calculate their density density(γ _n,m ,p) based on the nearest neighbors of each sub-channel loss coefficient point group, where γ _n,m is the mth The nth point of the sub-channel, from which the outlier score of each point group is calculated; then the average density of the neighbors of the point is calculated, and the average relative density of the point is calculated using the following formula:

In the formula, N(γ _n,m ,p) is the p-nearest neighbor point group of the target γ _n,m . This quantity indicates whether γ _n,m is in a denser or sparser neighborhood than its neighbors, and is taken as the outlier score for γ _n,m . Then the detected outliers are eliminated, and the remaining loss coefficient points are averaged at the same time, and the expression is:

In the formula, is the loss coefficient of the final m-th sub-channel, γ _n,m is the estimated signal loss coefficient on the m-th sub-channel in the n-th data packet, and k is the remaining loss coefficient points after removing outliers on each sub-channel . Finally, the loss coefficient of the signal propagation model of 30 sub-channels is obtained, expressed as:

Finally, the estimated sub-channel channel loss coefficient is brought into Equation (1), and finally the signal propagation model of 30 different path attenuation coefficients is obtained, expressed as:

In the formula, P _R,m is the signal energy received on the mth sub-channel, P _0,m is the signal energy value at the distance of 1m from the target on the mth sub-channel, is the path loss coefficient of the final mth subchannel.

The step 7) carries out target to the sub-channel distance estimation of AP, specifically includes:

According to the method as claimed in claim 2, a sub-channel signal propagation model is obtained. The AP receives multiple sets of data from the target, and obtains the CSI matrix shown in formula (2). The signal energy value of each subchannel is calculated separately, and its expression is:

In the formula, CSI _m is the energy value of the signal received by the mth sub-channel, csi _i,m is the CSI value received by the mth sub-channel in the i-th data packet, n is the number of received data packets, conj(·) To take the conjugate. Bring the signal energy values of the obtained 30 sub-channels into the sub-channel signal propagation model obtained in claim 2 to obtain 30 different distance values, expressed as:

d=[d ₁ , d ₂ , . . . , d ₃₀ ] (11).

Claims (2)

1. a subchannel estimation ranging method of OFDM, is characterized in that, comprises the following steps: 1) In the parameter estimation stage, a calibration point is randomly selected in the ranging environment where the target is located, and the distance from the selected calibration point to the Access Point (AP) is measured at the same time; 2) The target transmits data at the selected punctuation position, and the AP receives the arriving data, and extracts the sub-channel state information (Channel State Information, CSI) from it; 3) Filter the sub-channel state information obtained in step 2) using a Butterworth filter, suppress high-frequency noise, and obtain denoised sub-channel state information; 4) In combination with the sub-channel CSI obtained in the step 3) and the distance from the calibration point to the AP, the signal propagation model is parameterized, and finally the sub-channel signal propagation model is obtained; 5) In the distance estimation stage, the target can be at any position in the ranging area and send data, and at the same time, the AP receives the data sent by the target, and extracts the sub-channel CSI from the received data; 6) using a Butterworth filter to perform a denoising operation on the sub-channel CSI obtained in step 5), to obtain filtered sub-channel CSI; 7) The sub-channel CSI obtained based on the step 6) is brought into the sub-channel signal propagation model to obtain the sub-channel distance; 8) Clustering the sub-channel distances obtained in step 7), using the coordinates of the centroids as the distance from the target source to the AP. 2. based on the sub-channel estimation ranging method of a kind of OFDM described in claim 1, it is characterized in that: in described step 4), use sub-channel estimation method and the outlier detection algorithm based on relative density to obtain sub-channel signal Spread model. Specifically include: At present, the logarithmic-normal model is used to represent the path attenuation of wireless signals in indoor environments, and its expression is: P _R ＝P ₀ -10γlg(d) (1) In the formula, P _R is the received signal energy value, P ₀ is the signal energy value at a distance of 1m from the target, d is the distance between the target to be measured and the AP, and γ is the path loss coefficient. The higher the degree of signal loss. First, the AP receives the data sent by the target source at the selected punctuation point, extracts the sub-channel CSI from the received data, and at the same time filters and de-noises the obtained sub-channel CSI information, and forms the CSI matrix with the de-noised CSI information. The representation is: In the formula, csi _n,m represents the csi value of the mth subchannel in the received nth data packet. Then each sub-channel is brought into formula (1) as an independent channel, and the energy of the received signal in formula (1) is expressed as: P _n,m =csi _n,m ×conj(csi _n,m ) (3) In the formula, P _n,m is the signal energy value of the mth sub-channel in the received nth data packet, and conj(·) is the conjugate. The matrix of the obtained loss coefficient is expressed as: where γ _n,m is the estimated signal loss coefficient on the mth subchannel in the nth data packet. Then, the loss matrix is divided according to the number of sub-channels, that is, the number of columns of the matrix, and 30 sub-channel loss coefficient point groups are obtained, expressed as: γmatrix＝[γ ₁ ,γ ₂ ,…,γ ₃₀ ] (5) In the formula, γ _m is a vector composed of loss coefficients of the mth sub-channel. Use the relative density-based outlier detection algorithm to detect the abnormality of each sub-channel point group, and then remove the detected outliers, and average the remaining loss coefficient points at the same time, and its expression is: In the formula, is the loss coefficient of the final m-th sub-channel, γ _n,m is the estimated signal loss coefficient on the m-th sub-channel in the n-th data packet, and k is the remaining loss coefficient points after removing outliers on each sub-channel . Finally, the loss coefficient of the signal propagation model of 30 sub-channels is obtained, expressed as: Finally, the estimated sub-channel channel loss coefficient is brought into Equation (1), and finally the signal propagation model of 30 different path attenuation coefficients is obtained, expressed as: In the formula, P _R,m is the signal energy received on the mth sub-channel, P _0,m is the signal energy value at the distance of 1m from the target on the mth sub-channel, is the path loss coefficient of the final mth subchannel.