A signal depth compression and direct localization method, system, device and medium

Abstract

A signal depth compression and direct positioning method, system, device and medium, the method is: by arranging at least three observation stations in two-dimensional space, synchronously observing the signal emitted by the target, using Hadamard matrix for sampling compression and 1-bit quantization means for quantization compression, realizing the depth compression processing of the observed signal; using the characteristics of Hadamard matrix and the phase invariance of 1-bit compression, avoiding the complex signal reconstruction process, without extracting position parameters such as time delay, directly establishing a cost function from the depth compressed data using the retained time delay relationship to determine the position of the target; the system, device and medium can realize signal depth compression and direct positioning based on the above method; the present application can significantly shorten the data amount of signal transmission and simplify the processing process, and is suitable for positioning scenes with limited communication bandwidth and the need for fast response; on the premise of ensuring positioning accuracy, the signal data amount is greatly compressed, and the communication bandwidth requirement of each station receiving signal transmission to the center station is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electronic information technology, and particularly relates to a signal depth compression and direct positioning method, system, device and medium. BACKGROUND

[0002] Compressive Sensing (CS) theory is a novel signal acquisition and processing theory, which breaks through the limitation of the traditional Nyquist sampling theorem. Through the use of signal sparsity, it can accurately recover the signal at a much lower Nyquist sampling rate. This theory has shown great application potential in wireless communication, radar systems, image processing and other fields.

[0003] Recent research and patent applications have disclosed a series of direct positioning methods based on data compression. Patent application "Non-reconstruction direct positioning method based on mobile receiver data compression" (application number: CN202311041714.2) provides a non-reconstruction direct positioning method based on mobile receiver data compression. This method uses the characteristics of Hadamard matrix to directly estimate the source position in the compressed measurement domain without signal reconstruction and time difference and frequency difference parameter extraction. Patent application "Compressed sensing signal direct positioning method" (application number CN202311126528.9) provides a direct positioning method based on compressed sensing signal. By constructing a unique compressed sensing matrix, it can directly locate the radiation source without reconstructing the compressed sensing signal. "Method, device and system for directly positioning a stationary radiation source based on compressed observations", patent application (application number CN202311580347.3) provides a method, device and system for directly positioning a stationary radiation source under the condition that the main station is uncompressed and the auxiliary station signal is compressed. Without signal reconstruction and time difference and frequency difference parameter extraction, direct positioning can be achieved in the mixed observation domain.

[0004] Although there are some direct positioning methods based on data compression, the data compression depth of these methods is insufficient, and the compression rate is limited by the design of the compression matrix. They cannot meet the real-time data transmission and direct positioning requirements under the condition of limited communication bandwidth, and their compression multiple and positioning method need to be improved. SUMMARY

[0005] In order to overcome the above prior art, the purpose of the present application is to provide a signal depth compression and direct positioning method, system, device and medium, by arranging at least three observation stations in two-dimensional space, synchronously observing the signal emitted by the target, using Hadamard matrix for sampling compression and 1-bit quantization means for quantization compression, realizing the depth compression processing of the observation signal; using the characteristics of Hadamard matrix and the phase invariance of 1-bit compression, avoiding the complex signal reconstruction process, without extracting the position parameters such as time delay, directly establishing the cost function from the depth compressed data using the reserved time delay relationship to determine the position of the target; the present application can significantly shorten the data amount of signal transmission, simplify the processing process, and is suitable for positioning scenes with limited communication bandwidth and the need for fast response, greatly compressing the signal data amount under the premise of ensuring the positioning accuracy, and reducing the communication bandwidth requirement of each station receiving signal transmission to the center station.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0007] A signal depth compression and direct positioning method, at least 3 observation stations are arranged in two-dimensional space and initialized: the number of observation stations, the position coordinates of each observation station, the unified sampling frequency and sampling number of all observation stations, the sampling compression ratio and the target position search space are determined; then, the signals emitted by the target are observed by using multiple observation stations to form a group of received signal vectors containing the information emitted by the target; Hadamard matrix is used as the sampling compression matrix, Hadamard matrix is multiplied with the received signal vector to complete the sampling compression; the sampling compressed signal is quantized by 1-bit, only the sign information of each sampling point is reserved, forming a sampling-quantization joint depth compressed signal; the observation station transmits the sampling-quantization joint depth compressed signal to the center station for data collection and processing for processing: the time delay relationship after sampling compression is recovered by using Hadamard matrix; the cost function about the position of the target is established through the sampling-quantization joint depth compressed signal; finally, the grid search method is used to exhaustively search the cost function in the predetermined search space, and the maximum eigenvalue of the cost function is found; the position coordinates of the target are estimated by solving the target position corresponding to the maximum eigenvalue of the cost function.

[0008] A signal depth compression and direct positioning method, specifically comprising the following steps:

[0009] Step 1: At least 3 observation stations are arranged at any position in two-dimensional space respectively, and initialized, the number of observation stations, the position coordinates of each observation station, the unified sampling frequency and sampling number of all observation stations, the sampling compression ratio of all observation stations and the target position search space are determined;

[0010] Step 2: All observation stations determined in step 1 synchronously observe the signal emitted by the target at a set time interval, collect a set of received signal vectors containing the information emitted by the target, and provide raw signal data for the entire positioning process;

[0011] Step 3: Design a Hadamard matrix as a sampling compression matrix according to the sampling compression ratio determined in step 1 HM×N , multiply the Hadamard matrix with the received signal vector in step 2 for sampling compression, and then perform 1-bit quantization compression on the sampling compressed signal through a sign function to form a deep compressed signal after sampling-quantization joint compression;

[0012] Step 4: Each observation station transmits the deep compressed signal after sampling-quantization joint compression in step 3 to the central station for constructing a cost function about the target position.

[0013] Step 5: Use the grid search method to iteratively search the cost function in step 4 in the predetermined search space [x start ,x end ] and [y start ,y end ] to find the maximum eigenvalue of the cost function; find the target position corresponding to the maximum eigenvalue of the cost function, and the accurate estimation of the target position coordinates can be achieved.

[0014] The specific method of step 1 is to set the observation stations at any position in space, initialize to determine the number of observation stations L and L≤3, the two-dimensional position coordinates of the lth observation station p l , l = 1, 2,..., L; determine the uniform sampling frequency F s and the number of sampling points N = 2 n of all observation stations, n is a non-negative integer; determine the compression ratio N / M which can be divided by 4, where M = 2 m is the number of compressed sampling points, m is a non-negative integer; determine the target position search space as [x start ,x end ] in the x-axis direction and [y start ,y end ] in the y-axis direction.

[0015] The specific method of step 2 is that all observation stations synchronously observe the signal emitted by the target at a set time interval, and the received signal of the target observed by the lth observation station at time t is:

[0016] r l (t) = b l (t) s(t-τ l ) + ω l (t), (1)

[0017] where b l is unknown complex path loss, s(t) is target transmitted signal, τ l is time delay of signal propagation from transmission to observation station l, ω l (t) is independent complex Gaussian white noise generated during propagation, satisfying and assuming for each observation station, the sampled interval is T s = 1 / F s The received signal vector formed after Nyquist sampling is:

[0018] r l = b l Q l s+ω l , (2)

[0019] Each vector and matrix in equation (2) is:

[0020]

[0021]

[0022] The specific method of step 3 is:

[0023] According to the compression ratio N / M determined in step 1, the first M rows of the N×N Hadamard matrix form the sampling compression matrix H M×N , which has the following characteristics:

[0024]

[0025] The sampling compression matrix H M×N The signal after sampling compression of the original signal obtained in step 2 is:

[0026] y l = H M×N r l , (11)

[0027] Then, the sampling compression signal is quantized and compressed by 1-bit, which is realized by the sign function of equation (12):

[0028]

[0029] The cost function constructed in step 4 is:

[0030] C(p) = N·[sign(Re{R c}) + j·sign(Im{R c})], (13)

[0031] Where p represents the search space, i.e., the set of coordinate points traversed when calculating the cost function, and represents the candidate target locations.

[0032]

[0033] τ ij =τ i (p)-τ j (p), i, j ∈ {1, ... L} (15)

[0034] The signal whose time delay relationship was recovered from the sampling compression was obtained using the relationship shown in equation (16):

[0035]

[0036] The method in step 5 is as follows:

[0037] In the pre-defined search space [x start ,x end ]、[y start ,y end The cost function is calculated and searched within the [x] area; first, the search grid size is set to g, which is the traversal step size, and the search space [x] is divided according to g. start ,x end ]、[y start ,y end ] is divided into [(x end -x start ) / g+1][(y end -y start) / g+1] grid points; take the coordinates p of each grid point. g Substitute into the calculation cost function C(p) g Search cost function C(p) g The coordinates of the largest eigenvalue This refers to the estimation of the target's position coordinates;

[0038] Iterative search is performed based on the target's position coordinate estimation process: the grid size for the first round of search is set to g1, and the search space... The internal cost function is calculated and searched to obtain the target location estimate. by Determine a new search space for the center. The search grid size is g2; and so on. After multiple rounds of searching, the estimated coordinates of the target are finally obtained.

[0039] A signal depth compression and direct positioning system, comprising:

[0040] The signal receiving module is used for synchronously sampling signals from the target by the distributed observation station in step 2, and obtaining an original received signal vector, so as to ensure that time delay information contained in signals of each station is consistent with corresponding space propagation;

[0041] The deep compression module is used for designing a Hadamard matrix according to the compression rate determined in step 1 in step 3, multiplying the received signal vector from the left to realize sampling compression of the signal, and performing 1-bit quantization compression on the signal after sampling compression through a sign function, so as to obtain a signal after mixed sampling-quantization deep compression, significantly reduce the data amount of transmission, and ensure that the time delay relationship is not destroyed.

[0042] The cost function construction module is used for correlating the deep compression signal with the target position in a time difference relationship in step 4, establishing a cost function about the target position, obtaining a cost function value corresponding to each grid point, and reflecting a probability that the target is located at the position, so as to take the grid point as a candidate solution of the target position estimation.

[0043] The position estimation module is used for searching the target position corresponding to the maximum value of the cost function output by the cost function construction module in step 5 through a specified search space range and a layer-by-layer grid division strategy, and efficiently determining an accurate estimation of the target position.

[0044] A signal deep compression and direct positioning device, specifically comprising:

[0045] The memory is used for storing a computer program.

[0046] The processor is used for executing the computer program to realize the signal deep compression and direct positioning method in steps 1 to 5.

[0047] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to perform signal deep compression and direct positioning based on the signal deep compression and direct positioning method in steps 1 to 5.

[0048] Compared with the prior art, the present application has the following advantages:

[0049] 1、The present application realizes deep compression of the received signal by using a Hadamard matrix and 1-bit quantization, if the original signal has N sampling points and each point is B-bit, after being compressed by the Hadamard matrix to M points and quantized to 1-bit, the compression ratio can reach BN / M times, which greatly reduces the data amount of signals transmitted from each observation station to the center station, significantly reduces the data transmission pressure, and is suitable for positioning scenes with limited communication bandwidth such as extreme field and channel congestion.

[0050] 2、The application guarantees that the time delay relationship is not destroyed in the compression process through the characteristics of the Hadamard matrix and 1-bit quantization, so that even if the original signal is deeply compressed, the time delay information contained therein can still be used for accurate estimation of the target position.

[0051] 3、The application directly establishes a cost function about the target position through the time delay relationship contained in the deeply compressed signal, without the need to recover the original signal from the compressed signal, directly estimates the target position, avoids the signal reconstruction and intermediate parameter estimation process, simplifies the data processing flow, and improves the positioning efficiency.

[0052] In summary, the application arranges at least three observation stations in a two-dimensional space, synchronously observes the signals emitted by the target, uses the Hadamard matrix for sampling compression and the 1-bit quantization means for quantization compression, realizes deep compression processing of the observation signals, uses the characteristics of the Hadamard matrix and the phase invariance of 1-bit compression to avoid the complex signal reconstruction process, without the need to extract position parameters such as time delay, directly uses the preserved time delay relationship to establish a cost function from the deeply compressed data to determine the position of the target, can significantly shorten the data amount of signal transmission and simplify the processing process, is suitable for positioning scenes with limited communication bandwidth and the need for fast response, greatly compresses the signal data amount under the premise of ensuring positioning accuracy, and reduces the communication bandwidth requirement of each station for receiving signal transmission to the center station. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a schematic diagram of a direct positioning scene of the application.

[0054] Figure 2 is a schematic diagram of an embodiment scene of the application.

[0055] Figure 3 is a position spectrum of the cost function of the application.

[0056] Figure 4 is a target positioning performance diagram of the application under different compression ratios.

[0057] Table 1 is a positioning error comparison table of the application under different compression ratios and 1-bit quantization.

[0058] Table 2 is a CRB value table of the application under different signal-to-noise ratios. DETAILED DESCRIPTION

[0059] The application will be described in detail below in combination with the drawings and specific embodiments.

[0060] As Figure 1As shown, a signal deep compression and direct positioning method, at least 3 observation stations are arranged in a two-dimensional space and initialized: determine the number of observation stations, the position coordinates of each observation station, the sampling frequency and the sampling number of all observation stations, the sampling compression ratio and the target position search space; then, the signals emitted by the target are observed by multiple observation stations to form a set of received signal vectors containing the information emitted by the target; the Hadamard matrix is used as the sampling compression matrix, the Hadamard matrix is multiplied by the received signal vector to complete the sampling compression; the sampling-quantization joint deep compression signal is formed by performing 1-bit quantization on the sampling compressed signal and retaining only the sign information of each sampling point; the observation stations transmit the sampling-quantization joint deep compression signal to the central station for processing data to process: the time delay relationship after sampling compression is recovered by using the Hadamard matrix; the cost function about the target position is established through the sampling-quantization joint deep compression signal; finally, the maximum eigenvalue of the cost function is searched in the predetermined search space by using the grid search method; the position coordinates of the target are accurately estimated by solving the target position corresponding to the maximum eigenvalue of the cost function.

[0061] As shown, Figure 2 , the embodiment takes 3 position known observation stations and 1 target needing positioning in a two-dimensional space as an example, the position coordinates of the three observation stations are (0, 0), (2000, 100) and (0, 2500) (unit: meter); the real two-dimensional coordinates of the initial position of the target are (4500, 3000) (unit: meter).

[0062] A signal deep compression and direct positioning method, the specific steps are as follows:

[0063] Step 1: Set the observation stations to be located at any position in a two-dimensional space, initialize to determine the number of observation stations L=3, determine the position coordinates of each observation station as (0, 0), (2000, 100) and (0, 2500) (unit: meter); determine the sampling rate F s =2 23 Hz and the observation sampling number N=512 of all observation stations; determine the sampling compression ratio as 2, 4. Each original sampling point is a 64-bit double-precision floating point number. The embodiment needs to estimate the position of the target.

[0064] Step 2: 3 observation stations simultaneously observe the signals emitted by the target to form the original observation signal vectors r1∈á 512×1 , r2∈á 512×1 , r3∈á 512×1 . There are 1536 sampling data points, and the total data size is 12kB.

[0065] Step 3: Construct the Hadamard matrix H512×512 When the compression ratio is 2 and 4, the compression matrices are H 256×512 and H 128×512 respectively. The sampled compressed signals are denoted as

[0066] y l = H 256×512 r l ∈á 256×1 , (N / M = 2), l = 1, 2, 3

[0067] y l = H 128×512 r l ∈á 128×1 , (N / M = 4), l = 1, 2, 3

[0068] Next, the y l is quantized and compressed, and the compressed signal is denoted as

[0069]

[0070] It can be calculated that when the compression ratio is 2 and 4, the data size of the sampled and quantized compressed signal is 96 bytes and 48 bytes respectively, and the total compression ratio is 128 times and 256 times respectively.

[0071] Step 4: Substitute the compressed data into the cost function

[0072] C(p) = N·[sign(Re{R c}) + j·sign(Im{R c})],

[0073] The cost function value at the grid point p can be calculated.

[0074] Step 5: Construct the search space [2500, 6500], [1000, 5000] to calculate the cost function, set the initial traversal step size g = 1000, and reduce the step size to 1 / 5 of the last round every round. After multiple rounds of search, the position coordinate estimation of the target is obtained. The cost function of the first round of search is shown in Figure 3 .

[0075] Through simulation experiments, the performances of the target position estimation of the non-reconstruction direct positioning method of the present application and Hadamard compression under different compression strategies and different signal-to-noise ratios are compared. The root mean square error of the target position estimation is used as the evaluation standard for evaluation. The theoretical minimum estimation error CRLB when only using Hadamard matrix compression is also compared to better evaluate the precision loss caused by the fusion of 1-bit quantization. The simulation results are shown in Tables 1, 2 and Figure 4The data compression ratio can be increased by 64 times by introducing quantization compression when the compression ratio is the same, and the performance loss is only 0.4-1 times. Compared with the CRLB, the root mean square error of the target position determined by the application after sampling-quantization mixed compression can be controlled within 2 times of the theoretical performance boundary. It can be seen that the application significantly improves the data compression ratio compared with the prior art, which is 64 times higher than the Hadamard matrix sampling compression method; while greatly reducing the transmission data volume, it ensures a small performance loss, and the performance loss of the non-reconstruction direct positioning method of the Hadamard compression is not more than 1 times. Therefore, the application innovatively combines Hadamard sampling and 1-bit quantization to realize deep compression of data sampling-quantization, and simultaneously proposes a direct positioning method based on deep compression data of sampling-quantization, which greatly improves the positioning accuracy and significantly reduces the loss of positioning accuracy caused by deep data compression.

[0076] Table 1

[0077]

[0078] Table 2

[0079]

[0080] Further, the embodiment of the application provides a signal deep compression and direct positioning system, comprising:

[0081] The signal receiving module is used for, in step 2, synchronously sampling the signal from the target by the distributed observation station to obtain the original received signal vector, which can ensure that the time delay information contained in the signal of each station is consistent with the corresponding space propagation;

[0082] The deep compression module is used for, in step 3, designing the Hadamard matrix according to the compression rate determined in step 1 initialization, left multiplying the received signal vector to realize sampling compression of the signal; 1-bit quantization compression is performed on the signal after sampling compression through a sign function; the signal after sampling-quantization mixed deep compression is obtained, which significantly reduces the transmission data volume while ensuring that the time delay relationship is not destroyed;

[0083] The cost function construction module is used for, in step 4, correlating the deep compression signal and the target position with the time difference relationship to establish a cost function about the target position, obtaining the cost function value corresponding to each grid point, which can reflect the probability that the target is located at the position, and the grid point is used as a candidate solution for the target position estimation;

[0084] The position estimation module is used for, in step 5, searching the target position corresponding to the maximum value of the cost function output by the cost function construction module through the specified search space range and the layer-by-layer grid division strategy, which can efficiently determine the accurate estimation of the target position.

[0085] Further, the embodiment of the present application provides a signal depth compression and direct positioning device, which specifically comprises:

[0086] a memory for storing a computer program;

[0087] a processor for executing the computer program to realize the signal depth compression and direct positioning method in steps 1-5.

[0088] Further, the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to perform signal depth compression and direct positioning based on the signal depth compression and direct positioning method in steps 1-5.

Claims

1. A method for signal depth compression and direct positioning, characterized in that, At least three observation stations are arranged in a two-dimensional space and initialized: the number of observation stations, the position coordinates of each observation station, the uniform sampling frequency and number of samples for all observation stations, the sampling compression ratio, and the target location search space are determined; then, the signals emitted by the target are observed using multiple observation stations to form a set of received signal vectors containing the information emitted by the target; the Hadamard matrix is ​​used as the sampling compression matrix, and the Hadamard matrix is ​​multiplied with the received signal vector to complete the sampling compression; the sampled compressed signal is quantized 1-bit, retaining only the symbol information of each sampling point, forming a sampling-quantization joint deep compression signal; the observation stations transmit the sampling-quantization joint deep compression signal to the central station for data aggregation and processing: the Hadamard matrix is ​​used to recover the time delay relationship after sampling compression; A cost function for the target location is established by sampling-quantization combined with deep compression of the signal; finally, the cost function is exhaustively searched within a predetermined search space using a grid search method to find the maximum eigenvalue of the cost function. The target's position coordinates are estimated by solving for the target position corresponding to the largest eigenvalue of the cost function.

2. The signal depth compression and direct positioning method according to claim 1, characterized in that, Specifically, the following steps are included: Step 1: Arrange at least 3 observation stations at arbitrary locations in a two-dimensional space and initialize them, determining the number of observation stations, the position coordinates of each observation station, the uniform sampling frequency and number of samples for all observation stations, the sampling compression ratio for all observation stations, and the target location search space. Step 2: All observation stations determined in Step 1 synchronously observe the signals emitted by the target at set time intervals, and collect a set of received signal vectors containing the information emitted by the target to provide raw signal data for the entire positioning process; Step 3: Design the Hadamard matrix as the sampling compression matrix H based on the sampling compression ratio determined in Step 1. M×N The Hadamard matrix is ​​multiplied with the received signal vector from step 2 for sampling compression; then the sampled and compressed signal is quantized and compressed by a sign function to form a deeply compressed signal with joint sampling and quantization. Step 4: Each observation station transmits the depth compressed signal, which has been sampled and quantized in Step 3, to the central station to construct a cost function about the target location; Step 5: Using the grid search method, search the predetermined search space [x start ,x end ]、[y start ,y end The cost function from step 4 is iteratively searched to find the maximum eigenvalue of the cost function; once the target position corresponding to the maximum eigenvalue of the cost function is found, the accurate estimation of the target position coordinates can be achieved.

3. The signal depth compression and direct positioning method according to claim 2, characterized in that, The specific method of step 1 is as follows: set the observation station to be located at any position in space, initialize the number of observation stations to be L and L≥3, and set the two-dimensional position coordinates of the l-th observation station to be p. l Let l = 1, 2, ..., L; determine a uniform sampling frequency F for all observation stations. s And the number of sampling points N=2 n n is a non-negative integer; the compression ratio is determined to be N / M and divisible by 4, where M = 2. m m is the number of compressed sampling points, where m is a non-negative integer; the target location search space is defined in the x-axis direction [x start ,x end ], y-axis direction [y start ,y end ].

4. The signal depth compression and direct positioning method according to claim 2, characterized in that, The specific method of step 2 is as follows: all observation stations synchronously observe the signal emitted by the target according to a set time interval. The target received signal observed by the l-th observation station at time t is: r l (t)=b l (t)s(t-τ l )+ω l (t), (1) Among them, b l For unknown complex path attenuation, s(t) is the target transmitted signal, and τ is the signal transmitted via the target. l ω is the time delay of the signal propagating from the transmitter to the observation station l. l (t) represents the independent complex Gaussian white noise generated during propagation, satisfying... And assumptions for each observation station After sampling interval T s =1 / F s The received signal vector formed after Nyquist sampling is: rl=b l Q l s+ω l , (2) The vectors and matrices in equation (2) are as follows:

5. The signal depth compression and direct positioning method according to claim 2, characterized in that, The specific method for step 3 is as follows: Based on the compression ratio N / M determined in step 1, the first M rows of the N×N Hadamard matrix are used to construct the sampling compression matrix H. M×N It has the following characteristics: Sampling compression matrix H M×N The signal obtained by sampling and compressing the original signal acquired in step 2 is as follows: y l =H M×N r l , (11) Then, the sampled compressed signal is quantized and compressed using 1-bit quantization, implemented using the sign function of equation (12):

6. The signal depth compression and direct positioning method according to claim 2, characterized in that, The cost function constructed in step 4 is as follows: C(p)=N·[sign(Re{R c })+j·sign(Im{R c })],(13) Where p represents the search space, i.e., the set of coordinate points traversed when calculating the cost function, and represents the candidate target locations. t ij =t i (p)-t j (p),i,j∈{1,...L} (15) The signal whose time delay relationship was recovered from the sampling compression was obtained using the relationship shown in equation (16):

7. The signal depth compression and direct positioning method according to claim 2, characterized in that, The method in step 5 is as follows: In the pre-defined search space [x start ,x end ]、[y start ,y end The cost function is calculated and searched within the [x] area; first, the search grid size is set to g, which is the traversal step size, and the search space [x] is divided according to g. start ,x end ]、[y start ,y end ] is divided into [(x end -x start ) / g+1][(y end -y start [) / g+1] grid points; take the coordinates p corresponding to each grid point. g Substitute into the calculation cost function C(p) g Search cost function C(p) g The coordinates of the largest eigenvalue This refers to the estimation of the target's position coordinates; Iterative search is performed based on the target's position coordinate estimation process: the grid size for the first round of search is set to g1, and the search space... The internal cost function is calculated and searched to obtain the target location estimate. by Determine a new search space for the center. and search grid size g2 Similarly, after multiple rounds of searching, the estimated coordinates of the target are finally obtained.

8. A system based on the signal depth compression and direct positioning method according to any one of claims 2 to 5, characterized in that, include: The signal receiving module is used in step 2 to synchronously sample the signal from the target through distributed observation stations to obtain the original received signal vector, which can ensure that the time delay information contained in the signal of each station is consistent with the corresponding spatial propagation. The deep compression module is used in step 3 to design the Hadamard matrix based on the compression ratio determined in step 1, and to multiply the received signal vector by the left to achieve sampling compression of the signal; the sampled and compressed signal is quantized and compressed by 1-bit through the sign function; the sampled-quantized hybrid deep compressed signal is obtained, which significantly reduces the amount of data transmitted, while ensuring that the time delay relationship is not destroyed. The cost function construction module is used in step 4 to establish a cost function for the target position by correlating the deep compressed signal with the target position through a time difference relationship, and to obtain the cost function value corresponding to each grid point, which can reflect the probability that the target is located at that position. This grid point serves as a candidate solution for the target position estimation. The location estimation module is used in step 5 to search for the target location corresponding to the maximum value of the cost function output by the cost function construction module by specifying the search space range and the layer-by-layer grid division strategy. This can efficiently determine the accurate estimate of the target location.

9. An apparatus based on the signal depth compression and direct positioning method according to any one of claims 2 to 5, characterized in that, Specifically, it includes: Memory, used to store computer programs; A processor is used to implement the signal depth compression and direct positioning method described in steps 1 to 5 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can perform signal depth compression and direct positioning based on the signal depth compression and direct positioning method according to any one of claims 1 to 7.

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