A 5G passive radar low-altitude target positioning method based on particle filter

Abstract

The present invention relates to the technical field of external radiation source radar positioning, and specifically to a low-altitude target positioning method for 5G external radiation source radar based on particle filter, which includes the following steps: S1, signal synchronization between the transceiver stations; S2, echo signal processing; S3, establishment of the target state transition model and measurement model; S4, particle initialization; S5, calculation of the pseudo-range error between the target and the transceiver stations; S6, particle weight update; S7, particle resampling; S8, target state extraction. The present invention uses a positioning method based on time difference to obtain pseudo-range information, takes the pseudo-range error between the target and the 5G base stations and receiving stations participating in the positioning as the measurement value, obtains the preliminary positioning and pseudo-range information as prior information by processing the received echo signal, constructs the target state transition model and measurement model, estimates the position of the target to be measured through the particle filter algorithm, and finally realizes target positioning, reducing the influence of clutter and interference on target positioning in the low-altitude complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of external radiation source radar positioning, and specifically to a low-altitude target positioning method for 5G external radiation source radar based on particle filter. Background Art

[0002] In recent years, the rapid development of low-altitude targets such as unmanned aerial vehicles has posed a certain threat to the safety of the low-altitude area. Detecting and positioning such targets is a key link in the supervision and governance of the low-altitude area.

[0003] An external radiation source radar refers to a radar that does not emit electromagnetic wave signals by itself, but uses third-party radiation source signals to detect targets. Compared with traditional radars, external radiation source radars have advantages such as strong concealment, low cost, strong anti-interference ability, environmental friendliness, and spectrum resource saving, and have gradually become one of the important sensing means for target detection.

[0004] Compared with traditional opportunity illumination sources such as traditional frequency modulation signals, 5G signals have advantages such as wider bandwidth and higher carrier frequency. At the same time, the dense distribution of 5G base stations makes the signal coverage range wider, and multi-angle and full-range target illumination can be realized. Therefore, using the existing 5G base station facilities to detect and position low-altitude targets has certain advantages.

[0005] Particle filter is an optimal recursive Bayesian filtering algorithm based on Monte Carlo simulation. Its core is to represent the state vector of interest as a set of random samples with weights, namely particles. Based on the measurement, by adjusting the weights and positions of the particles, samples that conform to the actual distribution are obtained, and the sample mean is used as the system state estimate. As an effective non-linear filtering algorithm, the particle filter has advantages such as high accuracy, fast convergence, and no need for linearization processing of the state equation, and is not restricted by linearization errors and Gaussian environments, and is applicable to the case where the system equation is non-linear and the noise is non-Gaussian.

[0006] Common positioning methods for multi-station external radiation source radars include DOA positioning, TDOA positioning, and FDOA positioning, etc. Directly using these methods to position targets in a complex low-altitude environment with low signal-to-noise ratio will result in positioning ambiguity. Summary of the Invention

[0007] The purpose of the present invention is to provide a low-altitude target positioning method for 5G external radiation source radar based on particle filter to solve the problems raised in the above background art.

[0008] The technical solution of the present invention is: a low-altitude target positioning method for 5G external radiation source radar based on particle filter, including the following steps:

[0009] S1. Signal synchronization between transceiver stations: Achieve high-precision time synchronization of signals between all transceiver stations participating in positioning through optical fiber channels;

[0010] S2. Echo signal processing: The signal receiving end of the external radiation source radar system uses a reference channel and a monitoring channel to receive the direct wave and the target echo signal respectively, and processes the received signal to obtain the time difference between the direct wave and the target echo;

[0011] S3. Establishment of the target state transition model and the measurement model: Taking the position coordinates of the target to be measured at time k as the state quantity and the pseudo-range error Δρ between the target and the transceiver station at time k i,k as the measurement value, construct the state transition model and the measurement model of the target to be measured;

[0012] S4. Particle initialization: Construct a set of N particles composed of particle states and particle weights and perform particle initialization;

[0013] S5. Calculation of the pseudo-range error between the target and the transceiver station: Using the time difference of arrival positioning method, calculate the pseudo-range information between the transceiver station and the target to be measured from the time difference between the direct wave and the target echo obtained by echo signal processing, and calculate the pseudo-range error in combination with the estimated position of the target;

[0014] S6. Particle weight update: Update the weights of the generated particles with the obtained measurement data set and calculate the normalized particle weights;

[0015] S7. Particle resampling: Use the number of effective particles to describe the degree of particle depletion and compare it with the threshold number of particles. If it is less than the threshold number of particles, resample the particles, otherwise end the loop;

[0016] S8. Target state extraction: Extract the target state from the particle set to obtain the position of the low-altitude target after positioning optimization.

[0017] Preferably, S3 includes setting the state of the target at the k-th moment as x k , then its state transition model is expressed as: x k = f k (x k-1 ) + v k ,

[0018] where, f k (.) represents the state transition function at the k-th moment, and v k represents the motion process noise; the measurement model expression corresponding to the target at the k-th moment is: z k = h k (x k ) + w k ,

[0019] where, h k (.) represents the measurement function at the k-th moment, and w k represents the measurement noise, taking the target P at time ku The position coordinate x k = [x u,k , y u,k , z u,k T is a state variable, where x u,k , y u,k , z u,k are the spatial coordinate points of the target to be measured at the k-th moment;

[0020] Take the pseudo-range error data set between the 5G base stations and the receiving station participating in the positioning at the k-th moment as the measurement value, where N sta is the number of base stations participating in the positioning at the k-th moment, and Δρ i,k is the pseudo-range error between the target and the i-th transceiver station;

[0021] From the start time of observation to the k-th moment, the state set of the target to be measured is X 1:k = {x1, x2, …, x k} and the measurement value set is Z k = {z1, z2, …, z k}.

[0022] Preferably, S4 specifically includes constructing a set containing N particles and initializing it where represents the state of the i-th particle at the initial moment, is the weight of this particle at this time, and is expressed as follows:

[0023] Preferably, S5 includes that at the k-th moment, the passive radar positioning system consists of N sta 5G base stations participating in the positioning and a receiving station, and specifically includes the following steps:

[0024] S51. Using the position coordinate of the target to be measured at the k-th moment as the state variable and the pseudo-range error Δρ i,k between the target and the transceiver station at the k-th moment as the measurement value for modeling: Suppose that at the k-th moment, the position coordinates of the 5G base stations participating in the positioning are respectively and the pseudo-ranges between them and the target are respectively The position coordinate of the receiving station is P r (x r , y r , z r ), and the pseudo-range between it and the target is l r,k , then the sum of the pseudo-ranges of the 5G base stations participating in the positioning and the receiving station can be expressed as: ρ i,k = l i,k + l r,k i = 1, 2, …, N sta ;​

[0025] S52. Construct the state transition model of the target to be measured: The distances between the base station and the receiving station are respectively The position coordinates of the target to be measured are P u (x u,k , y u,k , z u,k ). The time differences between the direct wave and the target echo obtained from the echo signal processing are respectively The speed of light is represented by c. According to the relationship Δτ i,k ×c = ρ i,k -d i i = 1, 2, …, N sta , the sum of the pseudoranges between the 5G base stations and the receiving station participating in the positioning of the target to be measured at time k can be expressed as: ρ i,k = Δτ i,k ×c + d i i = 1, 2, …, N sta ;

[0026] S53. Construct the measurement model of the target to be measured: Due to the existence of measurement noise, multiple possible target positions will be obtained according to the geometric relationship of the spatial position. Take the average of all the obtained possible target position coordinate values as the initial target estimated position. Then, at the target estimated position the sum of the pseudoranges between the 5G base stations and the receiving station participating in the positioning is expressed as:

[0027] Then the pseudorange error is expressed as:

[0028] S54. Output the pseudorange error measurement set at time k: Finally, the pseudorange error measurement set measured by multiple stations at time k is:

[0029] Preferably, S6 specifically includes updating the weights of the generated particles according to the obtained measurement data set as follows:

[0030] Among them, is the likelihood function, is the importance density function, is the prior density function. If the importance density function is equal to the prior density function , the particle weight is expressed as:

[0031] In the low-altitude environment, assuming that the pseudorange errors between each station and the target are independent of each other, the likelihood function is expressed as: Then, the particle weight is expressed as:

[0032] The normalized particle weights are expressed as:

[0033] Preferably, S7 includes using the number of effective particles as an index to describe the degree of particle shortage, and its estimated value is expressed as:

[0034] The estimated value of the threshold number of particles is expressed as:

[0035] If N eff <N th , then resample the particles; otherwise, record the set of effective particles, and let

[0036] Preferably, S8 includes extracting the target state x from the formula k =[x u,k ,y u,k ,z u,k T , and obtaining the optimized low-altitude target position P u (x u,k ,y u,k ,z u,k ) coordinates, and repeating this process until the target trajectory ends.

[0037] The present invention provides an improved method for locating low-altitude targets of 5G passive radar based on particle filtering. Compared with the prior art, the following improvements and advantages are achieved:

[0038] The present invention uses a positioning method based on time difference to obtain pseudo-range information, takes the pseudo-range error between the target and the 5G base stations and receiving stations participating in the positioning as the measurement value, processes the received echo signals to obtain preliminary positioning and pseudo-range information as prior information, constructs a target state transition model and a measurement model, estimates the position of the target to be measured through the particle filter algorithm, and finally realizes target positioning, reducing the influence of clutter and interference in the low-altitude complex environment on target positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further explained below with reference to the drawings and embodiments:

[0040] Figure 1 is a schematic diagram of multi-station single-target positioning of 5G passive radar of the present invention;

[0041] Figure 2 is a flow chart of the positioning method of the present invention;

[0042] Figure 3 is a flow chart of the particle filter algorithm of the present invention. DETAILED DESCRIPTION OF THE INVENTION​

[0043] The present invention will be described in detail below. The technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0044] The present invention provides an improved low-altitude target positioning method for 5G external radiation source radar based on particle filter. The technical solution of the present invention is as follows:

[0045] As Figures 1 - 3 shown, a low-altitude target positioning method for 5G external radiation source radar based on particle filter includes the following steps:

[0046] S1. Signal synchronization between transceiver stations: High-precision time synchronization of signals between all transceiver stations participating in positioning is achieved through optical fiber channels;

[0047] S2. Echo signal processing: The signal receiving end of the external radiation source radar system uses a reference channel and a monitoring channel to receive direct wave and target echo signals respectively, and processes the received signals to obtain the time difference between the direct wave and the target echo;

[0048] S3. Establishment of target state transition model and measurement model: Taking the position coordinates of the target to be measured at time k as the state quantity, and the pseudo-range error between the target and the transceiver station at time k as the measurement value Δρ, construct the state transition model and measurement model of the target to be measured. Specifically, assume that the state of the target at the k-th moment is x i,k , then its state transition model is expressed as: x k = f k (x k ) + v k-1 , k ,

[0049] where, f k (.) represents the state transition function at the k-th moment, and v k represents the motion process noise; the measurement model expression corresponding to the target at the k-th moment is: z k = h k (x k ) + w k ,

[0050] where, h k (.) represents the measurement function at the moment, and w k represents the measurement noise. Take the position coordinates x u of the target P k at time k = [x u,k , y u,k , zu,k T is a state variable, where x u,k , y u,k , z u,k are the spatial coordinate points of the target to be measured at the k-th moment;

[0051] Take the pseudo-range error data set between the 5G base stations participating in the positioning at the k-th moment and the i-th transceiver station as the measurement value, where N sta is the number of base stations participating in the positioning at the k-th moment, and Δρ i,k is the pseudo-range error between the target and the i-th transceiver station at the k-th moment;

[0052] From the start time of observation to the k-th moment, the state set of the target to be measured is X 1:k ={x1, x2, …, x k},and the measurement value set is Z k ={z1, z2, …, z k};

[0053] S4. Particle initialization: Construct a set of N particles composed of particle states and particle weights and perform particle initialization. Specifically, construct a set containing N particles and initialize it where represents the state of the i-th particle at the initial moment, is the weight of this particle at this time, which is expressed as follows:

[0054] S5. Calculation of the pseudo-range error between the target and the transceiver station: Adopt the time difference of arrival positioning method, calculate the pseudo-range information between the transceiver station and the target to be measured from the time difference between the direct wave and the target echo obtained by echo signal processing, and calculate the pseudo-range error in combination with the estimated position of the target. Specifically, at the k-th moment, the external radiation source radar positioning system consists of N sta 5G base stations participating in the positioning and a receiving station, which specifically includes the following steps:

[0055] S51. Use the position coordinates of the target to be measured at the k-th moment as the state variable, and the pseudo-range error Δρ i,k between the target and the i-th transceiver station at the k-th moment as the measurement value for modeling: Suppose that at the k-th moment, the position coordinates of the 5G base stations participating in the positioning are respectively and the pseudo-ranges between them and the target are respectively The position coordinates of the receiving station are P r (x r , y r , z r ), and the pseudo-range between it and the target is l r,k , then the sum of the pseudo-ranges of the 5G base stations participating in the positioning and the receiving station can be expressed as: ρ i,k = l​i,k +l r,k i = 1, 2, …, N sta ;

[0056] S52. Construct the state transition model of the target to be measured: The distances between the base station and the receiving station are respectively The position coordinates of the target to be measured are P u (x u,k , y u,k , z u,k ). The time differences between the direct wave and the target echo obtained from the echo signal processing are respectively The speed of light is denoted as c. According to the relationship Δτ i,k ×c = ρ i,k -d i i = 1, 2, …, N sta , the sum of the pseudoranges between the 5G base stations and the receiving station participating in the positioning of the target to be measured at time k can be expressed as: ρ i,k = Δτ i,k ×c + d i i = 1, 2, …, N sta ;

[0057] S53. Construct the measurement model of the target to be measured: Due to the existence of measurement noise, multiple possible target positions will be obtained according to the geometric relationship of the spatial positions. The average of all possible target position coordinate values obtained is taken as the initial target estimation position. Then, at the target estimation position , the sum of the pseudoranges between the 5G base stations and the receiving station participating in the positioning is expressed as:

[0058]

[0059] Then the pseudorange error is expressed as:

[0060] S54. Output the pseudorange error measurement set at time k: Finally, the pseudorange error measurement set measured by multiple stations at time k is obtained as:

[0061] S6. Particle weight update: Update the weights of the generated particles with the obtained measurement data set and calculate the normalized particle weights. Specifically, it includes using the obtained measurement data set to update the weights of the generated particles, which is expressed as follows:

[0062] Among them, is the likelihood function, is the importance density function, is the prior density function. If the importance density function is equal to the prior density function , the particle weight is expressed as:

[0063] In the low-altitude environment, assuming that the pseudo-range errors between each station and the target are independent of each other, the likelihood function is expressed as:

[0064] Then, the particle weights are expressed as:

[0065] The normalized particle weights are expressed as:

[0066] S7. Particle resampling: Use the number of effective particles to describe the degree of particle depletion and compare it with the threshold number of particles. If it is less than the threshold number of particles, resample the particles; otherwise, end the loop. Specifically, use the number of effective particles as an indicator to describe the degree of particle depletion, and its estimated value is expressed as:

[0067] The estimated value of the threshold number of particles is expressed as:

[0068] If N eff <N th , resample the particles; otherwise, record the set of effective particles, and let where represents the set of effective particles at the k-th moment, represents the state of the i-th particle at the k-th moment;

[0069] S8. Target state extraction: Extract the target state from the particle set to obtain the low-altitude target position after positioning optimization. Specifically, extract the target state x from the formula k =[x u,k ,y u,k ,z u,k T , and obtain the coordinates of the low-altitude target position P u (x u,k ,y u,k ,z u,k ). Repeat this process until the target trajectory ends.

[0070] Based on the above method, a positioning method based on time difference is used to obtain pseudo-range information. The pseudo-range errors between the target and the 5G base stations and receiving stations participating in the positioning are used as measurement values. Preliminary positioning and pseudo-range information are obtained by processing the received echo signals as prior information. A target state transition model and a measurement model are constructed, and the position of the target to be measured is estimated through the particle filter algorithm, finally realizing target positioning and reducing the influence of clutter and interference in the low-altitude complex environment on target positioning.

[0071] ​The foregoing description enables those skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A low-altitude target positioning method for 5G passive radar based on particle filter, characterized in that: It includes the following steps: S1. Signal synchronization between transceiver stations: High-precision time synchronization of signals between all transceiver stations participating in positioning is achieved through an optical fiber channel; S2. Echo signal processing: At the signal receiving end of the external radiation source radar system, the reference channel and the monitoring channel are respectively used to receive the direct wave and the target echo signal, and the received signals are processed to obtain the time difference between the direct wave and the target echo; S3. Establishment of the target state transition model and measurement model: Taking the position coordinates of the target to be measured at time k as the state quantity and the pseudo-range error Δρ between the 5G base station and the transceiver participating in the positioning at time k as the measurement value, construct the state transition model and measurement model of the target to be measured; i,k ​ S4. Particle initialization: A set of N particles composed of particle states and particle weights is constructed and particle initialization is performed; S5. Calculation of the pseudo-range error between the target and the transceiver station: The time difference between the direct wave and the target echo obtained by echo signal processing is used to calculate the pseudo-range information between the transceiver station and the target to be measured by using the time difference of arrival positioning method, and the pseudo-range error is calculated in combination with the estimated position of the target; S6. Particle weight update: The weights of the generated particles are updated by the obtained measurement data set and the normalized particle weights are calculated; S7. Particle resampling: The degree of particle depletion is described by using the effective number of particles as an index and compared with the threshold number of particles. If it is less than the threshold number of particles, the particles are resampled, otherwise the loop ends; S8. Target state extraction: The target state is extracted from the particle set to obtain the position of the low-altitude target after positioning optimization.

2. The method for low-altitude target positioning of a 5G passive radar based on particle filter according to claim 1, wherein: Let the state of the target at the k-th moment be x k , then its state transition model is expressed as: x k = f k (x k-1 ) + v k , where, f k (.) represents the state transition function at the k-th moment, v k represents the process noise of motion; the measurement model expression corresponding to the target at the k-th moment is: z k = h k (x k ) + w k , Among them, h k (.) represents the measurement function at the k-th moment, w k represents the measurement noise, and the position coordinates x u of the target P at the k-th moment are taken. k = [x u,k , y u,k , z u,k T is the state quantity, where x u,k , y u,k , z u,k are the spatial coordinate points of the target to be measured at the k-th moment;​ Obtain the pseudo-range error data set between the 5G base stations participating in positioning at the k-th moment and the i-th transceiver station group as the measured value, where N sta is the number of base stations participating in positioning at the k-th moment, and Δρ i,k is the pseudo-range error between the 5G base stations participating in positioning at the k-th moment and the i-th transceiver station group; From the start time of observation to the k-th moment, the set of states of the target to be measured is X 1:k ={x1, x2, …, x k}, and the set of measurement values is Z k ={z1, z2, …, z k}.

3. A 5G external radiation source radar low-altitude target positioning method based on particle filter according to claim 2, characterized in that: The specific steps of S4 include constructing a set containing N particles and initializing it. Among them represents the state of the i-th particle at the initial moment. is the weight of the particle at this time, which is expressed as follows:

4. A method for low-altitude target positioning of 5G passive radar based on particle filter according to claim 3, characterized in that: The S5 includes that at the k-th moment, the external radiation source radar positioning system consists of N sta 5G base stations participating in positioning and a receiving station, and specifically includes the following steps: S51. Using the coordinates of the target position to be measured at time k as the state quantity, and the pseudo-range error Δρ between the 5G base stations participating in the positioning at time k and the i-th transceiver station as the measurement value for modeling: Assume that at time k, the position coordinates of the 5G base stations participating in the positioning are respectively i,k The pseudo-ranges between them and the target are respectively The position coordinates of the receiving station are P (x r , y r , z r ), and the pseudo-range between it and the target is l r r,k , then the sum of the pseudo-ranges between the 5G base stations participating in the positioning and the receiving station can be expressed as:​ ρ i,k = l i,k + l r,k i = 1, 2, …, N sta ; S52. Construct the state transition model of the target to be measured: The distances between the base station and the receiving station are respectively The position coordinates of the target to be measured are P u (x u,k , y u,k , z u,k ). The time differences between the direct wave and the target echo obtained by echo signal processing are respectively The speed of light is represented by c. According to the relationship Δτ i,k ×c = ρ i,k -d i i = 1, 2, …, N sta , the sum of the pseudoranges between the 5G base stations and the receiving station participating in positioning at time k can be expressed as: ρ i,k = Δτ i,k × c + d i i = 1, 2, …, N sta ; S53. Construct a measurement model for the target to be measured: Due to the existence of measurement noise, multiple possible target positions will be obtained according to the geometric relationship of spatial positions. Take the average of all possible target position coordinate values obtained as the initial target estimated position. Then, at the target estimated position the sum of the pseudoranges between the 5G base stations participating in positioning and the receiving station is expressed as: Then the pseudo-range error is expressed as: S54. Output the pseudo-range error measurement set at the k-th moment: Finally, the pseudo-range error measurement data set measured by multiple stations at the k-th moment is obtained as:

5. A low-altitude target positioning method for 5G external radiation source radar based on particle filter according to claim 4, characterized in that: The S6 specifically includes the obtained measurement data set Update the weights of the generated particles, expressed as follows: Among them, is the likelihood function, is the importance density function, is the prior density function. If the importance density function is equal to the prior density function then the particle weights are expressed as: In the low-altitude environment, assuming that the pseudo-range errors between each station and the target are independent of each other, the likelihood function is expressed as: Then, the particle weight is expressed as: The normalized particle weight is expressed as:

6. A method for low-altitude target positioning of a 5G external radiation source radar based on particle filtering according to claim 5, characterized in that: The said S7 includes using the effective number of particles as an index to describe the degree of particle depletion, and its estimated value is expressed as: The estimated value of the threshold number of particles is expressed as: If N eff < N th , resample the particles; otherwise, record the effective particle set and set 7. A low-altitude target positioning method for 5G external radiation source radar based on particle filter according to claim 6, characterized in that: The S8 includes the formula Extract the target state x k = [x u,k , y u,k , z u,k T , and obtain the optimized low-altitude target position P u (x u,k , y u,k , z u,k ) coordinates, and repeat this process until the target trajectory ends.​

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