A method for through-the-wall imaging based on a wireless communication system

Abstract

The application discloses a through-wall imaging method based on a wireless communication system. The wireless communication system is composed of a base station and a user terminal, wherein the base station is located outdoors, the user terminal is located indoors, wireless communication is carried out between the base station and the user terminal, and channel response data is acquired through the process. Through modeling of the wireless channel and design of an imaging algorithm, the method realizes through-wall imaging based on the channel response data, compared with the existing through-wall imaging method, the method can be completely based on the existing wireless communication system, and can be integrated into the next generation wireless communication system to form a perception communication integrated system, thereby avoiding additional costs of the imaging system hardware, and the accuracy of the through-wall imaging is relatively high, and the method provides a new idea for realization and application of the through-wall imaging function.

Description

A through-wall imaging method based on a wireless communication system Technical Field

[0001] This invention belongs to the field of wireless communication, and specifically relates to a through-wall imaging method based on a wireless communication system. Background Technology

[0002] Through-the-Wall Imaging (TWI) utilizes the property that electromagnetic waves can penetrate obstructing media to achieve imaging detection of targets behind walls, below the ground, or in dense foliage, including tasks such as ranging, localization, identification, tracking, and two-dimensional and three-dimensional imaging.

[0003] TWI technology is widely used in urban counter-terrorism and disaster relief missions, such as detecting moving targets in urban bunkers, tunnels, or under rubble. The widespread application of through-wall imaging has made it a research hotspot. Currently, the mainstream through-wall imaging methods generally belong to radar mechanisms, which have advantages such as low susceptibility to environmental influences and high imaging resolution. However, they also suffer from problems such as expensive equipment, inconvenient use, and limited application scenarios, restricting their further promotion.

[0004] Existing wireless communication systems typically operate at GHz frequencies, and their signals have a certain ability to penetrate the walls of rooms in a home. Furthermore, they are widely deployed and inexpensive. Therefore, it is worthwhile to consider researching the process and methods for through-wall imaging based on wireless communication systems.

[0005] After determining the basic architecture of the through-wall imaging system, achieving efficient and accurate through-wall imaging still faces several challenges. One is the extremely weak energy of the effective portion of the channel containing target information. In this system, the primary channel's path does not pass through the target object, therefore it does not contain indoor target information and can be considered interference. The secondary channel contains all target information, but its proportion of channel energy is very small; therefore, algorithms must be designed to amplify the effective signal and reduce interference. Another challenge is the unknown nature of wall parameters. Specifically, the presence of a wall causes changes in the propagation path and speed of electromagnetic waves, and also reduces signal strength. Without compensation for the wall's influence, artifacts, positional shifts, or loss of detail in the imaging results can occur. When the wall's position and electromagnetic properties are unknown, the wall compensation problem becomes even more complex. The through-wall imaging system must thoroughly consider these two issues and design corresponding processing schemes to achieve highly accurate through-wall imaging. Summary of the Invention

[0006] To address the shortcomings of the existing technologies, this invention provides a through-wall imaging method based on a wireless communication system. This method uses channel response data obtained by periodically performing channel estimation tasks on the communication system to achieve through-wall imaging. It does not require major modifications to the hardware or software of the existing communication system, making it an economical and efficient through-wall imaging method.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A through-wall imaging method based on a wireless communication system is proposed, comprising the following steps:

[0009] The base station in the wireless communication system receives wireless signals sent by user terminals in the same system, and performs channel estimation at the base station to obtain channel response data.

[0010] The environmental space and walls are discretized, and a mathematical model of channel response and environmental target is constructed using the electromagnetic wave propagation mechanism.

[0011] Using the channel response data, and based on the mathematical model of the channel response and the environmental target, the through-wall imaging problem is transformed into a generalized compressed sensing optimization problem.

[0012] The mathematical model transformed into the generalized compressed sensing optimization problem is processed by global search and least squares-based wall parameter estimation to achieve wall impact compensation, thus transforming the generalized compressed sensing optimization problem into a standard compressed sensing problem.

[0013] The standard compressed sensing problem is solved by solving the inverse problem based on approximate message passing, thus achieving through-wall imaging of environmental targets.

[0014] Furthermore, constructing the mathematical model of the channel response and the environmental objective specifically includes the following steps:

[0015] Step S21: Discretize the environment space and the wall. Let the size of the imaged environment space be L. L L W L H The size of the smaller cubes after division is l l , l w , l h Then there are a total of N = L in the entire environment. L / l l ×L W / l w ×L H / l h Individuals; using x n To represent the reflectance coefficient of the nth voxel, we use an N-dimensional reflectance coefficient vector x = [x1, ..., xn]. N ]T To represent the environment space to be perceived; similarly, the size of the wall is... Divided into use Let n represent the nth digit of the wall w The transmittance coefficient of a voxel, therefore, using N w dimensional vector To represent the entire wall;

[0016] Step S22: Using the electromagnetic wave propagation mechanism, from the m-th user, through the n-th user... w Channel response of the first scattering path to the base station from a wall block This can be expressed as:

[0017]

[0018] Where e is the natural constant and j is the imaginary unit. It is the amplitude of the channel. The phase of the channel depends on the wall parameter d. w Specifically expressed as

[0019]

[0020]

[0021] Where λ is the wavelength of the carrier signal. It is from the m-th user to the n-th user. w The distance between the blocks of wall From the nth w The distance from the wall to the base station antenna, σ is the radar cross-section of the object;

[0022] Step S23: Using the electromagnetic wave propagation mechanism, starting from the m-th user, passing through the n-th voxel, and penetrating the n-th voxel... w Channel response of the secondary scattering path to the base station from the wall block This can be expressed as:

[0023]

[0024] Specifically expressed as

[0025]

[0026]

[0027] Where, dm.n It is the distance from the m-th user to the n-th voxel. From the nth voxel to the nth voxel w The distance between the blocks of wall From the nth w The distance from the wall block to the base station antenna, where the latter two distances both depend on d. w ;

[0028] Step S24: The overall channel response is a combination of the primary and secondary channel responses. Based on the multipath channel model, a mathematical model of the channel response and the environmental target is constructed:

[0029] h c =h w +h+v

[0030] =H w ·y w +H·x+v

[0031] Among them, h c It is channel response data, h w H is the primary channel response, h is the secondary response, v is Gaussian white noise, and H is the secondary channel response. w It is a first-order channel matrix, y w H is the transmittance coefficient of the wall; H is the measurement matrix, which is calculated as follows: x is the scattering coefficient of the environmental target.

[0032] Furthermore, transforming the through-wall imaging problem into the generalized compressed sensing optimization problem specifically includes the following steps:

[0033] Using the channel response data, and based on the mathematical model of the channel response and the environmental target obtained in step S24, the through-wall imaging problem is transformed into a generalized compressed sensing optimization problem, expressed as follows:

[0034]

[0035] Where x represents the reflection coefficient vector of the environment, y w Let h represent the transmission coefficient vector of the wall, ||·||1, ||·||2 be the first norm and second norm of the vector, respectively. c H is the channel response, h′ is the channel response of the secondary channel, and H is the channel response of the secondary channel. w (d w H(d) is the channel response matrix of the primary scattering path calculated according to step S22. w The diagram shows the measurement matrix calculated according to steps S23 and S24, where ε is a slack variable and d is a slack variable. w This represents the wall's coordinates on the x-axis. It is the set of possible values ​​for the wall location, where D min and D max These are the minimum and maximum values, respectively, d w It is the interval of candidate values, when d is set w When small enough, it can approximate the actual position of the wall infinitely.

[0036] Furthermore, constructing the standard compressed sensing problem specifically includes the following steps:

[0037] Step S41: Based on the mathematical model transformed into a generalized compressed sensing optimization problem, ignoring the secondary channel h, a simplified model is obtained as follows:

[0038] h c =H w ·y w +v

[0039] Based on this simplified model, the subproblem of wall parameter estimation is obtained, as follows:

[0040]

[0041] Step S42, for the set Iterate through each element in the array and set the wall position d. i =D min +(i-1)· w The channel response h is calculated once according to step S22. w (d i The least squares algorithm is used to perform linear regression on c, specifically... Calculate the generated channel h w (d i The channel response data h in S1 c Vector similarity metrics, specifically including Save this data. After the traversal steps are completed, find the i-th position where the vector similarity index is maximized to obtain the estimated wall location. and estimated wall parameters

[0042] Step S43: Use the wall parameters estimated in step S42 and the formula in step S22 to estimate the primary channel, and subtract it from the overall channel response to obtain the estimated value of the secondary channel.

[0043] At the same time, the wall parameters estimated in step S42 should be followed. The measurement matrix H is corrected using the formula in step S23, transforming the generalized compressed sensing problem into a standard compressed sensing problem, expressed as:

[0044]

[0045] Furthermore, solving the standard compressed sensing problem using an inverse problem based on approximate message passing specifically includes the following steps:

[0046] The mathematical model of the standard compressed sensing problem is solved using the expectation-maximization-generalized approximate message passing algorithm based on approximate message passing. It should be noted that the measurement matrix H has some columns with small L2 norms. Therefore, column normalization preprocessing is required for the measurement matrix. After obtaining the sparse vector x, inverse normalization is also required. The result is the through-wall imaging result.

[0047] Furthermore, the base station in the wireless communication system should be located in an open outdoor area, and the user terminal can be located indoors or outdoors, but only the channel response data of the indoor user terminal will participate in the subsequent through-wall imaging process.

[0048] The beneficial effects of this invention are as follows: First, this invention considers a through-wall imaging method based on a wireless communication system. This system includes both user terminal devices distributed indoors and base station facilities located outdoors. Utilizing communication data between users and base stations, it achieves through-wall imaging of indoor targets without the need for expensive through-wall radar or similar equipment, making it an economical and effective solution. Second, this method models the through-wall imaging problem as a generalized compressed sensing optimization problem and divides the imaging process into two steps: wall parameter estimation and target imaging. In the wall parameter estimation stage, a global search and least squares parameter estimation method are used to estimate the wall's position and transmission coefficient. In the target imaging stage, wall parameter compensation is performed first, followed by solving an inverse problem. Therefore, it is a highly accurate through-wall imaging algorithm that can overcome the influence of unknown wall parameters to a certain extent. Compared to existing through-wall imaging algorithms, the through-wall imaging method based on a wireless communication system in this invention significantly improves the accuracy of environmental target imaging, providing an effective method for achieving through-wall imaging in mobile communication systems. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 is a schematic diagram of a wall-penetrating imaging system based on a wireless communication system provided in an exemplary embodiment;

[0051] Figure 2 shows the metric curve of a wall location estimation algorithm provided in an exemplary embodiment;

[0052] Figure 3 is an exemplary embodiment illustrating a comparison between the algorithm of the present invention and other through-wall imaging algorithms;

[0053] Figure 4 is a comparison of the imaging performance MSE of the algorithm of the present invention with that of other reconstruction algorithms under different SNR conditions provided by an exemplary embodiment.

[0054] Figure 5 is a graph showing the relationship between the MSE performance of the algorithm of the present invention and the number of user terminals (UEs) provided in an exemplary embodiment. Detailed Implementation

[0055] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0056] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0057] As shown in Figure 1, the scenario considered is an uplink communication scenario. A multi-antenna base station access point (AP) is deployed in an outdoor area, and multiple active single-antenna user terminal devices (UEs) exist simultaneously. Only UEs located indoors are considered. In this scenario, users send signals to the AP, which performs channel estimation and obtains channel response data. The calculation task for through-wall imaging is performed on a central server. It is noteworthy that the signals sent by users are affected by environmental objects and walls. Some signals travel directly from the UE to the AP, and these signals do not contain information about the target object. Other signals originate from the UE, are reflected by environmental targets, and then penetrate the wall to reach the AP, thus containing information about the environmental target.

[0058] In one embodiment, a through-wall imaging method based on a wireless communication system is provided, comprising the following steps:

[0059] Step S1: Use a base station in the wireless communication system to receive wireless signals sent by user terminals in the same system, and perform channel estimation at the base station to obtain channel response data;

[0060] In one embodiment, the base station in the wireless communication system should be located in an open outdoor area, and the user terminal can be located indoors or outdoors, but only the channel response data of the indoor user terminal will participate in the subsequent through-wall imaging process.

[0061] Step S2: Discretize the environmental space and walls, and use the electromagnetic wave propagation mechanism to construct a mathematical model of the channel response and the environmental target.

[0062] In one embodiment, constructing the mathematical model of the channel response and the environmental objective specifically includes the following steps:

[0063] Step S21: Discretize the environment space and the wall. Let the size of the imaged environment space be L. L L W L H The size of the smaller cubes after division is l l , l w , l h Then there are a total of N = L in the entire environment. L / l l ×L W / l w ×L H / l h Individuals; using x n To represent the reflectance coefficient of the nth voxel, we use an N-dimensional reflectance coefficient vector x = [x1, ..., xn]. N ] T To represent the environment space to be perceived; similarly, the size of the wall is... Divided into use Let n represent the nth digit of the wall w The transmittance coefficient of a voxel, therefore, using N w dimensional vector To represent the entire wall;

[0064] Step S22: Using the electromagnetic wave propagation mechanism, from the m-th user, through the n-th user... w Channel response of the first scattering path to the base station from a wall block This can be expressed as:

[0065]

[0066] Where e is the natural constant and j is the imaginary unit. It is the amplitude of the channel. The phase of the channel depends on the wall parameter d. w Specifically expressed as

[0067]

[0068]

[0069] Where λ is the wavelength of the carrier signal. It is from the m-th user to the n-th user. w The distance between the blocks of wall From the nth w The distance from the wall to the base station antenna, σ is the radar cross-section of the object;

[0070] Step S23: Using the electromagnetic wave propagation mechanism, starting from the m-th user, passing through the n-th voxel, and penetrating the n-th voxel... w Channel response of the secondary scattering path to the base station from the wall block This can be expressed as:

[0071]

[0072] Specifically, it can be expressed as follows:

[0073]

[0074]

[0075] Where, d m.n It is the distance from the m-th user to the n-th voxel. From the nth voxel to the nth voxel w The distance between the blocks of wall From the nth w The distance from the wall block to the base station antenna, where the latter two distances both depend on d. w ;

[0076] Step S24: The overall channel response is a combination of the primary and secondary channel responses. Based on the multipath channel model, a mathematical model of the channel response and the environmental target is constructed:

[0077] h c =h w +h+v

[0078] =H w ·y w +H·x+v

[0079] Among them, h c It is channel response data, h w H is the primary channel response, h is the secondary response, v is Gaussian white noise, and H is the secondary channel response. w It is a first-order channel matrix, y w H is the transmittance coefficient of the wall; H is the measurement matrix, which is calculated as follows: x is the scattering coefficient of the environmental target.

[0080] Step S3: Using channel response data, based on a mathematical model of channel response and environmental targets, the through-wall imaging problem is transformed into a generalized compressed sensing optimization problem;

[0081] In one embodiment, transforming the through-wall imaging problem into a generalized compressed sensing optimization problem specifically includes the following steps:

[0082] Using the channel response data, and based on the mathematical model of the channel response and the environmental target obtained in step S24, the through-wall imaging problem is transformed into a generalized compressed sensing optimization problem, expressed as follows:

[0083]

[0084] Where x represents the reflection coefficient vector of the environment, y w Let h represent the transmission coefficient vector of the wall, ||·||1, ||·||2 be the first norm and second norm of the vector, respectively. c H is the channel response, h′ is the channel response of the secondary channel, and H is the channel response of the secondary channel. w (d w H(d) is the channel response matrix of the first scattering path calculated according to step S22. w The diagram shows the measurement matrix calculated according to steps S23 and S24, where ε is a slack variable and d... w This represents the wall's coordinates on the x-axis. It is the set of possible values ​​for the wall location, where D min and D max These are the minimum and maximum values, respectively, d w It is the interval of candidate values, when d is set w When small enough, it can approximate the actual position of the wall infinitely.

[0085] Step S4 involves performing wall parameter estimation based on global search and least squares on the mathematical model transformed into a generalized compressed sensing optimization problem, thereby achieving wall impact compensation and transforming the generalized compressed sensing optimization problem into a standard compressed sensing problem.

[0086] In one embodiment, constructing a standard compressed sensing problem specifically includes the following steps:

[0087] Step S41: Based on the mathematical model transformed into a generalized compressed sensing optimization problem, ignoring the secondary channel h, a simplified model is obtained as follows:

[0088] h c =H w ·y w +v

[0089] Based on this simplified model, the subproblem of wall parameter estimation is obtained, as follows:

[0090]

[0091] Step S42, for the set Iterate through each element in the array and set the wall position d. i =D min +(i-1)· w Calculate the channel response h of the channel according to step S22. w (d i The least squares algorithm is used to perform linear regression on c, specifically... Calculate the generated channel h w (d i The channel response data h in S1 c Vector similarity metrics, specifically including Save this data. After the traversal steps are completed, find the i-th position where the vector similarity index is maximized to obtain the estimated wall location. and estimated wall parameters

[0092] Step S43: Use the wall parameters estimated in step S42 and the formula in step S22 to estimate the primary channel, and subtract it from the overall channel response to obtain the estimated value of the secondary channel.

[0093] At the same time, the wall parameters estimated in step S42 should be followed. The measurement matrix H is corrected using the formula in step S23, transforming the generalized compressed sensing problem into a standard compressed sensing problem, expressed as:

[0094]

[0095] Step S5: Solve the standard compressed sensing problem by solving the inverse problem based on approximate message passing to complete the through-wall imaging of environmental targets.

[0096] In one embodiment, solving the standard compressed sensing problem as an inverse problem based on approximate message passing specifically includes the following steps:

[0097] The mathematical model of the standard compressed sensing problem is solved using the expectation-maximization-generalized approximate message passing algorithm based on approximate message passing. It should be noted that the measurement matrix H has some columns with small L2 norms. Therefore, column normalization preprocessing is required for the measurement matrix. After obtaining the sparse vector x, inverse normalization is also required. The result is the through-wall imaging result.

[0098] Computer simulations, as shown in Figure 2, reveal a significant peak in the curve. One candidate value has a significantly higher similarity metric than the others, indicating the true location of the wall. This case demonstrates that the proposed wall parameter estimation algorithm exhibits good discriminative power, with approximately an order of magnitude difference between the true value and other candidate values, reducing the likelihood of incorrect location determinations and thus demonstrating high accuracy.

[0099] Figure 3 shows a typical simulation case. The upper figure is the set system scenario, which includes an AP, a wall, and some targets with a certain degree of complexity. The lower left figure is the imaging result of a comparison algorithm. This algorithm does not consider the influence of the wall at all and performs imaging directly, so its imaging performance is poor. The lower right figure is the imaging result of the algorithm of this invention, which has better performance.

[0100] Figure 4 illustrates that the environmental perception performance of the algorithm of this invention is significantly better than other algorithms, and the leading advantage increases with the increase of SNR. Figure 5 shows that the environmental perception performance of the method of this invention improves with the increase of the number of users.

[0101] In summary, this invention considers a through-wall imaging method based on a wireless communication system. This system includes both indoor user terminal devices and outdoor base station facilities. Utilizing communication data between users and base stations, it achieves through-wall imaging of indoor targets without the need for expensive through-wall radar or similar equipment, making it an economical and effective solution. Secondly, this method models the through-wall imaging problem as a generalized compressed sensing optimization problem and divides the imaging process into two steps: wall parameter estimation and target imaging. In the wall parameter estimation stage, a global search and least squares parameter estimation method are used to estimate the wall's position and transmission coefficient. In the target imaging stage, wall parameter compensation is first performed, followed by solving an inverse problem. Therefore, this is a highly accurate through-wall imaging algorithm that can overcome the influence of unknown wall parameters to a certain extent. Compared to existing through-wall imaging algorithms, the through-wall imaging method based on a wireless communication system in this invention significantly improves the accuracy of environmental target imaging, providing an effective method for achieving through-wall imaging in mobile communication systems.

[0102] The above are merely preferred embodiments of one or more embodiments of this specification and are not intended to limit the scope of one or more embodiments of this specification. Any modifications made within the spirit and principles of one or more embodiments of this specification are permitted.

Claims

1. A through-wall imaging method based on a wireless communication system, characterized in that, Includes the following steps: The base station in the wireless communication system receives wireless signals sent by user terminals in the same system, and performs channel estimation at the base station to obtain channel response data. The environmental space and the wall are discretized, and a mathematical model of channel response and environmental target is constructed using the electromagnetic wave propagation mechanism. Using the channel response data, based on the mathematical model of channel response and environmental target, the through-wall imaging problem is transformed into a generalized compressed sensing optimization problem. The mathematical model transformed into the generalized compressed sensing optimization problem is processed by wall parameter estimation based on global search and least squares to achieve wall influence compensation, and the generalized compressed sensing optimization problem is transformed into a standard compressed sensing problem. The standard compressed sensing problem is solved by solving the inverse problem based on approximate message passing to complete the through-wall imaging of the environmental target.

2. The through-wall imaging method based on a wireless communication system according to claim 1, characterized in that, Constructing the mathematical model of the channel response and the environmental target specifically includes the following steps: Step S21, discretizing the environmental space and the wall, assuming the size of the imaged environmental space is L. L L W L H The size of the smaller cubes after division is l l , l w , l h Then there are a total of N = L in the entire environment. L / l l ×L W / l w ×L H / l h Individuals; using x n To represent the reflectance coefficient of the nth voxel, we use an N-dimensional reflectance coefficient vector x = [x1, ..., xn]. N ] T To represent the environment space to be perceived; similarly, the size of the wall is... Divided into use Let n represent the nth digit of the wall w The transmittance coefficient of a voxel, therefore, using N w dimensional vector To represent the entire wall; Step S22, using the electromagnetic wave propagation mechanism, from the m-th user, through the n-th user... w Channel response of the first scattering path to the base station from a wall block This can be expressed as: Where e is the natural constant and j is the imaginary unit. It is the amplitude of the channel. The phase of the channel depends on the wall parameter d. w Specifically expressed as Where λ is the wavelength of the carrier signal. It is from the m-th user to the n-th user. w The distance between the blocks of wall From the nth w The distance from the wall to the base station antenna, σ is the radar cross-section of the object; Step S23, using the electromagnetic wave propagation mechanism, starting from the m-th user, passing through the n-th voxel, and penetrating the n-th voxel... w Channel response of the secondary scattering path to the base station from the wall block This can be expressed as: Specifically expressed as Where, d m.n It is the distance from the m-th user to the n-th voxel. From the nth voxel to the nth voxel w The distance between the blocks of wall From the nth w The distance from the wall block to the base station antenna, where the latter two distances both depend on d. w Step S24: The overall channel response is a combination of the primary and secondary channel responses. Based on the multipath channel model, a mathematical model of the channel response and the environmental target is constructed: h c =h w +h+v=H w ·y w +H·x+v where, h c It is channel response data, h w H is the primary channel response, h is the secondary response, v is Gaussian white noise, and H is the secondary channel response. w It is a first-order channel matrix, y w H is the transmittance coefficient of the wall; H is the measurement matrix, which is calculated as follows: x is the scattering coefficient of the environmental target.

3. The through-wall imaging method based on a wireless communication system according to claim 2, characterized in that, Transforming the through-wall imaging problem into the generalized compressed sensing optimization problem specifically includes the following steps: using the channel response data, based on the mathematical model of the channel response and the environmental target obtained in step S24, the through-wall imaging problem is transformed into a generalized compressed sensing optimization problem, expressed as follows: s.t.||h′-H(d w )·x||2≤εh′=h c -H w (d w )·y w Where x represents the reflection coefficient vector of the environment, y w Let h represent the transmission coefficient vector of the wall, ||·||1, ||·||2 be the first norm and second norm of the vector, respectively. c H is the channel response, h′ is the channel response of the secondary channel, and H is the channel response of the secondary channel. w (d w H(d) is the channel response matrix of the primary scattering path calculated according to step S22. w The diagram shows the measurement matrix calculated according to steps S23 and S24, where ε is a slack variable and d is a slack variable. w This represents the wall's coordinates on the x-axis. It is the set of possible values ​​for the wall location, where D min and D max These are the minimum and maximum values, respectively, d w It is the interval of candidate values, when d is set w When small enough, it can approximate the actual position of the wall infinitely.

4. The through-wall imaging method based on a wireless communication system according to claim 3, characterized in that, The construction of the standard compressed sensing problem specifically includes the following steps: Step S41, based on the mathematical model transformed into a generalized compressed sensing optimization problem, ignoring the secondary channel h, the simplified model is obtained as follows: h c =H w ·y w +v Based on this simplified model, the subproblem of wall parameter estimation is obtained, which is expressed as follows: Step S42, for the set Iterate through each element in the array and set the wall position d. i =D min +(i-1)·d w The channel response h is calculated once according to step S22. w (d i The least squares algorithm is used to perform linear regression on c, specifically... Calculate the generated channel h w (d i The channel response data h in S1 c Vector similarity metrics, specifically including Save this data. After the traversal steps are completed, find the i-th position where the vector similarity index is maximized to obtain the estimated wall location. and estimated wall parameters Step S43: Estimate the primary channel using the wall parameters estimated in step S42 and the formula in step S22, and subtract it from the overall channel response to obtain the estimated value of the secondary channel; simultaneously, estimate the secondary channel according to the wall parameters estimated in step S42. The measurement matrix H is corrected using the formula in step S23, transforming the generalized compressed sensing problem into a standard compressed sensing problem, expressed as:

5. The through-wall imaging method based on a wireless communication system according to claim 4, characterized in that, Solving the standard compressed sensing problem using an inverse problem based on approximate message passing specifically includes the following steps: The mathematical model of the standard compressed sensing problem is solved using the expectation maximization-generalized approximate message passing algorithm based on approximate message passing. It should be noted that the measurement matrix H has some columns with small L2 norms, so column normalization preprocessing is required for the measurement matrix. After obtaining the sparse vector x, inverse normalization is also required. The result obtained is the through-wall imaging result.

6. The through-wall imaging method based on a wireless communication system according to claim 1, characterized in that, The base station in the wireless communication system should be located in an open outdoor area, and the user terminal can be located indoors or outdoors, but only the channel response data of the indoor user terminal will participate in the subsequent through-wall imaging process.

Images