Imaging method based on neighborhood search and continuous interference elimination

Abstract

The invention relates to an imaging method based on neighborhood search and continuous interference elimination. The imaging method comprises the following steps: S1, acquiring channel frequency domain response data of a wireless channel; s2, determining the position of a scatterer in the environment; s3, generating an image plane and setting spatial pixel points; s4, generating a plurality of space sub-pixel points in the neighborhood of each space pixel point, and carrying out time delay and phase difference; s5, calculating a reconstruction signal according to the phase difference information; s6, selecting an imaging result point in the neighborhood of each spatial pixel point; step S7, removing a reconstruction signal of an imaging result point from the complete channel response data; step S8, repeating the step S4 to the step S7 for each space pixel point neighborhood in the space; and S9, setting normalized brightness information corresponding to the magnitude of the amplitude module value at the spatial position of each imaging result point to realize spatial imaging. Compared with the prior art, the method has the advantages of strong anti-interference capability, high imaging contrast, good compatibility with the shape of a target object and the like.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication and communication perception integration, and in particular to an imaging method based on neighborhood search and continuous interference elimination. Background Art

[0002] Communication-aware integration is one of the potential key technologies in the sixth generation of mobile communication system (6G) and has become a hot topic in current research. Its design concept is to achieve the integration of two independent functions, wireless communication and wireless perception, in the same system and make them mutually beneficial. On the one hand, the communication system can use the same spectrum resources and even reuse hardware devices and signal processing modules to complete various types of perception services. On the other hand, the perception results can be used to assist in the access and management of communications, improving service quality and communication efficiency.

[0003] In the integrated communication and perception system, perception is mainly reflected in the use of radio wave propagation mechanism to locate and image the scatterers distributed in the environment. With the development of future communication systems, higher frequency bands, wider bandwidths and larger antenna arrays make it possible to integrate wireless perception capabilities into communication systems.

[0004] However, at present, facing complex environments and challenging intelligent applications, the communication-perception integrated system that uses channel response to achieve perception imaging faces problems such as low parameter estimation resolution, unclear imaging edges, and insufficient imaging contrast. This makes it difficult to achieve accurate imaging of small targets in the environment. To address this problem, it is urgent to improve the existing imaging algorithm to improve the accuracy and contrast of imaging and suppress background noise to achieve more accurate target imaging and environmental perception. Summary of the invention

[0005] The purpose of the present invention is to provide an imaging method based on neighborhood search and continuous interference elimination.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] An imaging method based on neighborhood search and continuous interference elimination, comprising:

[0008] Step S1: Acquire channel frequency domain response data of a wireless channel;

[0009] Step S2: based on the channel frequency domain response data, estimate the multipath component parameters and determine the position of the scatterer in the environment;

[0010] Step S3: generating an image plane at the position of the scatterer and setting spatial pixel points;

[0011] Step S4: Based on neighborhood search, multiple spatial sub-pixel points are generated in the neighborhood of each spatial pixel point, and the time delay and phase difference from the receiving antenna to each spatial sub-pixel point are calculated according to the geometric information;

[0012] Step S5: according to the phase difference information and based on the maximum likelihood principle, the amplitude of the electric wave at each spatial sub-pixel point is estimated, and the reconstructed signal is calculated;

[0013] Step S6: In the neighborhood of each spatial pixel point, select the spatial sub-pixel point position and the corresponding amplitude with the minimum mean square error with the original signal as the imaging result point of the area corresponding to the spatial pixel point;

[0014] Step S7: According to the principle of continuous interference elimination, the reconstructed signal of the imaging result point in the area corresponding to the spatial pixel point is removed from the complete channel response data;

[0015] Step S8: Repeat steps S4 to S7 for each spatial pixel point neighborhood in the space until the traversal of all spatial pixel points is completed;

[0016] Step S9: at the spatial position of each imaging result point, normalized brightness information corresponding to the amplitude modulus value is set to achieve spatial imaging.

[0017] The step S2 comprises:

[0018] Step S2-1: Select a parameterized channel model;

[0019] Step S2-2: using a high-precision parameter estimation algorithm to estimate multipath parameters based on channel frequency domain response data;

[0020] Step S2-3: Determine the position of the scatterer in the environment based on the multipath parameters and in combination with the parameterized channel model.

[0021] The parameterized channel model is a spherical wave model, and the mathematical expression is:

[0022]

[0023] Where: y i,n,m (t) is the signal transmitted by the mth transmitting antenna and received by the nth receiving antenna in the i-th measurement period, L is the total number of paths, p Tx is the polarization mode of the transmitting antenna, p Rx is the polarization mode of the receiving antenna, and its value range is p Tx ,p Rx ∈{1,2}, represents two mutually orthogonal linear polarization directions, v lis the Doppler frequency of the lth path connecting the reference antenna of the transmitter and the reference antenna of the receiver, t i,n,m is the channel measurement time from the mth transmitting antenna to the nth receiving antenna in the i-th measurement cycle, τ l is the delay of the lth path connecting the reference antenna at the transmitter and the reference antenna at the receiver, Δτ Tx,m,l is the delay difference caused by the difference between the distance from the scatterer involved in the first hop of the path to the mth transmitting antenna and the distance to the transmitting reference antenna, Δτ Rx,n,l is the delay difference caused by the difference between the distance from the scatterer involved in the last hop of the path to the nth receiving antenna and the distance to the receiving reference antenna, is the p transmitted by the mth transmitting antenna Tx Polarization-based wave response, is the p received by the nth receiving antenna Rx Polarization-wise wave arrival response, Ω Tx,m,l is the wave departure direction of the mth transmitting antenna on the lth path, Ω Rx,n,l is the direction of arrival of the nth receiving antenna on the lth path, ω i,n,m (t) is the complex symmetric Gaussian noise component, and T is the duration of the measurement.

[0024] In step S3, at the position of the scatterer, based on the half-wavelength constraint of Abbe imaging theory, a uniform diffusion method is used to generate an imaging plane.

[0025] In step S4, based on neighborhood search, spatial sub-pixel points are uniformly generated in the neighborhood of each spatial pixel point, and the time delay and phase difference from the receiving antenna to each spatial sub-pixel point are calculated based on the known Cartesian coordinates of the receiving antenna and the spatial sub-pixel point.

[0026] In step S5, the amplitude of the spatial sub-pixel point is estimated according to the maximum likelihood principle through the response and phase difference information received by the antenna.

[0027] In step S9, the amplitude is normalized by mapping to the interval [0, 1] using linear normalization.

[0028] The interval k of the spatial sub-pixel points is:

[0029]

[0030] Where: λ is the wavelength of the radio wave, N interp Generates the number of sub-pixels on one side of a pixel's neighborhood.

[0031] An imaging device based on neighborhood search and continuous interference elimination comprises a memory, a processor, and a program stored in the memory. When the processor executes the program, the method described above is implemented.

[0032] A storage medium having a program stored thereon, wherein the program, when executed, implements the method according to any one of claims 1 to 8.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. By utilizing the channel characteristic information and generating sub-pixel points for neighborhood search, the local optimal solution under the regional spatial constraints is guaranteed, and the amplitude of multi-channel signals is estimated between multiple pixel areas based on the maximum likelihood principle, and crosstalk is reduced by updating implicit data.

[0035] 2. According to the maximum likelihood principle, the amplitude of the radio wave at each spatial sub-pixel point is estimated, and the reconstructed signal is calculated. The accuracy of the amplitude estimation of a pixel neighborhood is guaranteed through neighborhood search.

[0036] 3. According to the principle of continuous interference elimination, the reconstructed signal of the imaging result point in the area corresponding to the spatial pixel point is removed from the complete channel response data to ensure that the reconstructed signal no longer affects the subsequent estimation, which can more effectively suppress the crosstalk between pixels and achieve better imaging effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic flow chart of the main steps of the method of the present invention;

[0038] Figure 2 This is a schematic diagram of the distribution of spatial scatterers in the embodiment;

[0039] Figure 3 is the power-delay spectrum in the embodiment;

[0040] Figure 4 is the angle-power spectrum of the scatterer distribution in space in the embodiment;

[0041] Figure 5 This is the perception imaging result based on neighborhood search and continuous interference elimination in the present invention adopted in the embodiment. DETAILED DESCRIPTION

[0042] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0043] An imaging method based on neighborhood search and successive interference elimination, such as Figure 1 As shown, including:

[0044] Step S1: Acquire channel frequency domain response data of a wireless channel;

[0045] In this embodiment, the channel frequency domain response data may be obtained through propagation graph theory simulation or measurement using a virtual network analyzer in an actual measurement scenario.

[0046] Step S2: estimating multipath component parameters based on the channel frequency domain response data and determining the position of the scatterer in the environment, specifically including:

[0047] Step S2-1: Select a parameterized channel model;

[0048] In this embodiment, the parameterized channel model is a spherical wave model, and the mathematical expression is:

[0049]

[0050] Where: y i,n,m (t) is the signal transmitted by the mth transmitting antenna and received by the nth receiving antenna in the i-th measurement period, L is the total number of paths, p Tx is the polarization mode of the transmitting antenna, p Rx is the polarization mode of the receiving antenna, and its value range is p Tx ,p Rx ∈{1,2}, represents two mutually orthogonal linear polarization directions, v l is the Doppler frequency of the lth path connecting the reference antenna of the transmitter and the reference antenna of the receiver, t i,n,m is the channel measurement time from the mth transmitting antenna to the nth receiving antenna in the i-th measurement cycle, τ l is the delay of the lth path connecting the reference antenna at the transmitter and the reference antenna at the receiver, Δτ Tx,m,l is the delay difference caused by the difference between the distance from the scatterer involved in the first hop of the path to the mth transmitting antenna and the distance to the transmitting reference antenna, Δτ Rx,n,l is the delay difference caused by the difference between the distance from the scatterer involved in the last hop of the path to the nth receiving antenna and the distance to the receiving reference antenna, is the p transmitted by the mth transmitting antenna Tx Polarization-based wave response, is the p received by the nth receiving antenna Rx Polarization-wise wave arrival response, Ω Tx,m,l is the wave departure direction of the mth transmitting antenna on the lth path, Ω Rx,n,l is the direction of arrival of the nth receiving antenna on the lth path, ω i,n,m (t) is the complex symmetric Gaussian noise component, and T is the duration of the measurement.

[0051] Among them, Ω represents a unit direction vector, which is uniquely determined by its direction angle φ and pitch angle θ, and its expression is:

[0052] Ω=[sin(θ)cos(φ)sin(θ)sin(φ)cos(θ)] T .

[0053] Typically, the locations of the transmit reference antenna and the receive reference antenna coincide with the origin of the coordinate system of the transmitter and the receiver, respectively. Tx,m,l )、DoA(Ω Rx,n,l ) and the delay can be expressed as:

[0054]

[0055] in, represents the position of the transmitting reference antenna, represents the position of the receiving reference antenna, d Tx,l represents the distance of the scatterer involved in the first hop along the lth path from the transmitting reference antenna, d Rx,l represents the distance from the receiving reference antenna along the lth path to the scatterer involved in the last hop, represents the DoD of the transmitting reference antenna on the lth path, represents the DoA of the receiving reference antenna on the lth path. The ||·|| symbol represents the norm operation of the given parameters, and c represents the speed of light. It can be proved that when and Sometimes, there is and That is, when d Tx,l and d Rx,l Greater than d Rayleigh When , the spherical wavefront can be approximated as a plane wavefront. Compared with the plane wave model, the spherical wave model is more consistent with the characteristics of the radio wave itself and is more conducive to describing the non-steady-state characteristics of large array scenarios. When the antenna array size is relatively small, The value compared to The order of magnitude of can be ignored. The value compared to Likewise, it means that the DoD of the transmitting antenna and the DoA of the receiving antenna are no longer affected by the position of the array element in the antenna array, that is, the propagation characteristics of the radio wave are the same as those of the plane wave. The spherical wave model is a universal channel model, and the plane wave model is a simplified case under a smaller antenna array size. The spherical wave model is used as the channel model in the present invention.

[0056] Step S2-2: Use a high-precision parameter estimation algorithm to estimate multipath parameters based on the channel frequency domain response data.

[0057] After selecting the spherical wave channel model, the High Resolution Parameter Estimation (HRPE) algorithm is used to estimate the parameters of the L multipaths. In the Single-Input Single-Output (SIMO) scenario, the model can be simplified, and the unknown parameters that need to be estimated are:

[0058] θ=[θ1,…,θ l ,…,θ L ].

[0059] in, represents the parameter set on the lth path, which contains the spatial position information of the scatterer. The spatial alternating generalized expectation maximization (SAGE) algorithm is used to achieve high-precision parameter estimation. The estimation order and iterative update formula of each parameter are as follows:

[0060]

[0061] Where z(·) represents the likelihood function, represents the estimated delay value of the lth path, represents the estimated horizontal angle from the lth path to the Rx front, represents the estimated value of the elevation angle from the lth path to the Rx front. The parameter value can be estimated through continuous iteration. From the geometric relationship, it can be known that the position s of the scatterer involved in the last hop of the lth path is Rx,l The expression is:

[0062]

[0063] Step S2-3: Determine the position of the scatterer in the environment based on the multipath parameters and in combination with the parameterized channel model.

[0064] Step S3: generating an image plane at the position of the scatterer and setting spatial pixel points;

[0065] The generation of spatial pixel points is based on the center of the imaging plane, and the imaging plane size and initial step length are set. The initial step length is set to half a wavelength, thereby ensuring that the signals between multiple pixel neighborhoods can achieve continuous interference elimination.

[0066] Step S4: Based on the neighborhood search, multiple spatial sub-pixel points are generated in the neighborhood of each spatial pixel point, and the time delay and phase difference from the receiving antenna to each spatial sub-pixel point are calculated based on the geometric information, wherein specifically: based on the neighborhood search, spatial sub-pixel points are uniformly generated in the neighborhood of each spatial pixel point, and the time delay and phase difference from the receiving antenna to each spatial sub-pixel point are calculated based on the known Cartesian coordinates of the receiving antenna and the spatial sub-pixel point.

[0067] In this embodiment, for the generation of spatial sub-pixel points, the interpolation number N of the spatial sub-pixel points is defined as interp , the corresponding one-dimensional coordinates are uniformly generated and combined to obtain the spatial position of the spatial sub-pixel points. The spatial sub-pixel interval k can be expressed as:

[0068]

[0069] Where: λ is the wavelength of the radio wave, N interp Generates the number of sub-pixels on one side of a pixel's neighborhood.

[0070] In this embodiment, the phase difference from the spatial sub-pixel point to the receiving antenna is calculated as follows:

[0071] The carrier frequency is f, and the coordinates of the pth neighborhood space sub-pixel point are r p,q , the spatial coordinate of the nth receiving antenna is r Rx,n , the phase difference s from the pth neighborhood spatial sub-pixel point to the nth receiving antenna p,q,n It can be expressed as:

[0072] s p,q,n =exp{-j2πf(||r p,q -r Rx,n ||c -1 )}.

[0073] Here, the ||·|| symbol represents the norm operation of the given parameters, and c represents the speed of light.

[0074] Step S5: According to the phase difference information and based on the maximum likelihood principle, the amplitude of the electric wave at each spatial sub-pixel point is estimated, and the reconstructed signal is calculated.

[0075] Specifically, the maximum likelihood principle is used to calculate the amplitude of the spatial sub-pixel points based on the phase difference. Q sub-pixels are generated in the neighborhood of the pth initial pixel point, then the phase rotation vector of the qth sub-pixel point around the pth initial pixel point can be expressed as s p,q , whose expression is:

[0076]

[0077] in, It can be calculated by the spatial position information of the qth sub-pixel point around the pth initial pixel point. The complete data y can be expressed as:

[0078]

[0079] Among them, α p,q Implicit data through the complete data y To estimate, N is the number of Rx antennas, K is the number of frequency points. w follows Gaussian distribution, and the likelihood function can be expressed as:

[0080]

[0081] Let Λ(s p ;x p )right The partial derivative of is 0, and the complex amplitude of the qth sub-pixel around the pth initial pixel can be obtained. for:

[0082]

[0083] The amplitude estimate and phase rotation vector of the best matching sub-pixel point in the neighborhood of the pth initial pixel point can be selected by the value of the likelihood function, that is:

[0084]

[0085] After the amplitude estimation is completed, the implicit data needs to be updated, that is, the reconstructed signal components are removed from the implicit data so that they do not affect the subsequent estimation. The implicit data update process is:

[0086]

[0087] Step S6: In the neighborhood of each spatial pixel point, select the spatial sub-pixel point position and the corresponding amplitude with the minimum mean square error with the original signal as the imaging result point of the area corresponding to the spatial pixel point;

[0088] Step S7: According to the principle of continuous interference elimination, the reconstructed signal of the imaging result point in the area corresponding to the spatial pixel point is removed from the complete channel response data;

[0089] Step S8: Repeat steps S4 to S7 for each spatial pixel point neighborhood in the space until the traversal of all spatial pixel points is completed;

[0090] Step S9: At the spatial position of each imaging result point, the normalized brightness information of the corresponding amplitude modulus value is set to realize spatial imaging, wherein the amplitude is linearly normalized to the [0,1] interval according to the estimated spatial position of the pixel point and the local optimal estimate of the corresponding amplitude, and the normalized amplitude is placed at the corresponding spatial position to realize imaging of the target object.

[0091] The following is an experimental process to illustrate.

[0092] Figure 2In the schematic diagram of spatial scatterer distribution shown in FIG, according to the geometric position relationship between the transceiver in the scene and the scatterer distribution, it can be seen that the paths in the environment are all line-of-sight paths (Line of Sight, LOS), and the scene is a SIMO scene. This imaging method is reflective imaging. The transmitting antenna is a single antenna with a spatial coordinate of . The scatterer plane is parallel to the YOZ plane, and the X-axis coordinate is 0.1m. The receiving antenna is a 101×31 antenna array, and the array is parallel to the YOZ plane, centered at the origin. The shape of the scatterer in space is three equally spaced rectangles distributed along the Y-axis direction, with a length of 0.01m, a width of 0.005m, an interval of 0.01m, and a geometric center of . The frequency of the electromagnetic wave is GHz, the number of frequency points is 101, the carrier frequency is 140GHz, and the bandwidth is 4GHz. Through Figure 2 In the setting of the digital map, based on the synthetic channel simulation, the amplitude of the scatterer is set, and then the signal is synthesized according to the spatial phase information to synthesize the CIR of the channel and realize the simulation of the channel response. The simulated power-delay profile (PDP) is shown as Figure 3 shown.

[0093] In view of the above situation, the specific implementation process of the algorithm in the present invention is as follows Figure 1 As shown in the figure, this method realizes neighborhood search based on the principle of minimum mean square error by generating spatial sub-pixel points, and removes the reconstructed signal of the imaging result point in the area from the complete channel response data according to the SIC method, suppresses background noise, and realizes more precise imaging, as follows:

[0094] First, based on the CIR generated by simulation, the SAGE high-precision parameter estimation algorithm is used to estimate the parameter set The parameter set contains the spatial position information of the scatterer, and the spatial alternating generalized expectation maximization (SAGE) algorithm is used to achieve high-precision estimation of the parameters. The estimation order and iterative update formula of each parameter are as follows:

[0095]

[0096] Where z(·) represents the likelihood function, represents the estimated delay value of the lth path, represents the estimated horizontal angle from the lth path to the Rx front, represents the estimated elevation angle from the lth path to the Rx front. The parameter value can be estimated through continuous iteration. Since the amplitude of the radio wave at the scatterer position is directly set in the scene, the delay estimated by the SAGE algorithm is actually the delay from the scatterer to the Rx front. From the geometric relationship, it can be seen that the position s of the scatterer involved in the last hop of the lth path is Rx,l The expression is:

[0097]

[0098] By setting the number of paths to 3 and the number of iterations to 3, the angle-power spectrum of the scatterer distribution in space can be obtained as follows: Figure 4 shown. Figure 4 The equivalent scatterer positions of the main distribution can be seen in the figure, where the spatial position parameters of the main path are estimated to be [0.28ns, 0°, 90°]. By converting the spherical coordinate system to the Cartesian coordinate system, the main path position obtained by parameter estimation is [0.085, 0, 0].

[0099] Then, at the estimated position, parallel to the Rx array, an imaging plane of size 60×20 is generated, the pixel spacing is half a wavelength, and the imaging range is a rectangular area of ​​0.0643×0.0214. For each generated pixel, a neighborhood search method is used to uniformly generate multiple spatial sub-pixels in a pixel neighborhood. The sub-pixel spacing is set to 1 / 3 of the pixel neighborhood spacing, that is, 9 spatial sub-pixels will be uniformly generated in a pixel neighborhood. The phase of the generated sub-pixel at the spatial position is calculated, and the amplitude at the spatial sub-pixel is estimated according to the maximum likelihood principle. The carrier frequency is f, and the coordinates of the pth neighborhood spatial sub-pixel are r p,q , the spatial coordinate of the nth receiving antenna is r Rx,n , the phase difference s from the pth neighborhood spatial sub-pixel point to the nth receiving antenna p,q,n It can be expressed as:

[0100] s p,q,n =exp{-j2πf(||r p,q -r Rx,n ||c -1 )}.

[0101] Among them, the ||·|| symbol represents the norm operation of the given parameters, and c represents the speed of light. Through the maximum likelihood principle, the amplitude of the spatial sub-pixel point is calculated according to the phase difference. Q sub-pixels are generated in the neighborhood of the pth initial pixel point, then the phase rotation vector of the qth sub-pixel point around the pth initial pixel point can be expressed as s p,q , whose expression is:

[0102]

[0103] in, It can be calculated by the spatial position information of the qth sub-pixel point around the pth initial pixel point. The complete data y can be expressed as:

[0104]

[0105] Among them, α p,q Implicit data through the complete data y To estimate, N is the number of Rx antennas, K is the number of frequency points. w follows Gaussian distribution, and the likelihood function can be expressed as:

[0106]

[0107] Let Λ(s p ;x p )right The partial derivative of is 0, and the complex amplitude of the qth sub-pixel around the pth initial pixel can be obtained. for:

[0108]

[0109] The amplitude estimate and phase rotation vector of the best matching sub-pixel point in the neighborhood of the pth initial pixel point can be selected by the value of the likelihood function, that is:

[0110]

[0111] After estimating the sub-pixel amplitudes in the spatial neighborhood, a sub-pixel with the minimum mean square error is selected in each neighborhood as the imaging result point in this neighborhood.

[0112] Finally, after the amplitude estimation is completed, the implicit data is updated, that is, the reconstructed signal components are removed from the implicit data so that they do not affect the subsequent estimation. The implicit data update process is:

[0113]

[0114] According to the estimated pixel spatial position and the local optimal estimate of the corresponding amplitude, the amplitude is linearly normalized to the interval [0,1], and the normalized amplitude is placed at the corresponding spatial position to achieve the imaging of the target object. The final imaging result of the simulated object is shown in the figure Figure 5 shown.

[0115] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

Claims

1. An imaging method based on neighborhood search and continuous interference elimination, characterized in that: include: Step S1: Acquire channel frequency domain response data of a wireless channel; Step S2: based on the channel frequency domain response data, estimate the multipath component parameters and determine the position of the scatterer in the environment; Step S3: generating an image plane at the position of the scatterer and setting spatial pixel points; Step S4: Based on neighborhood search, multiple spatial sub-pixel points are generated in the neighborhood of each spatial pixel point, and the time delay and phase difference from the receiving antenna to each spatial sub-pixel point are calculated according to the geometric information; Step S5: according to the phase difference information and based on the maximum likelihood principle, the amplitude of the electric wave at each spatial sub-pixel point is estimated, and the reconstructed signal is calculated; Step S6: In the neighborhood of each spatial pixel point, select the spatial sub-pixel point position and the corresponding amplitude with the minimum mean square error with the original signal as the imaging result point of the area corresponding to the spatial pixel point; Step S7: According to the principle of continuous interference elimination, the reconstructed signal of the imaging result point in the area corresponding to the spatial pixel point is removed from the complete channel response data; Step S8: Repeat steps S4 to S7 for each spatial pixel point neighborhood in the space until the traversal of all spatial pixel points is completed; Step S9: at the spatial position of each imaging result point, normalized brightness information corresponding to the amplitude modulus value is set to achieve spatial imaging.

2. The imaging method based on neighborhood search and continuous interference elimination according to claim 1, characterized in that: The step S2 comprises: Step S2-1: Select a parameterized channel model; Step S2-2: using a high-precision parameter estimation algorithm to estimate multipath parameters based on channel frequency domain response data; Step S2-3: Determine the position of the scatterer in the environment based on the multipath parameters and in combination with the parameterized channel model.

3. The imaging method based on neighborhood search and continuous interference elimination according to claim 2, characterized in that: The parameterized channel model is a spherical wave model, and the mathematical expression is: Where: y i,n,m (t) is the signal transmitted by the mth transmitting antenna and received by the nth receiving antenna in the i-th measurement period, L is the total number of paths, p Tx is the polarization mode of the transmitting antenna, p Rx is the polarization mode of the receiving antenna, and its value range is p Tx ,p Rx ∈{1,2}, represents two mutually orthogonal linear polarization directions, v l is the Doppler frequency of the lth path connecting the reference antenna of the transmitter and the reference antenna of the receiver, t i,n,m is the channel measurement time from the mth transmitting antenna to the nth receiving antenna in the i-th measurement cycle, τ l is the delay of the lth path connecting the reference antenna at the transmitter and the reference antenna at the receiver, Δτ Tx,m,l is the delay difference caused by the difference between the distance from the scatterer involved in the first hop of the path to the mth transmitting antenna and the distance to the transmitting reference antenna, Δτ Rx,n,l is the delay difference caused by the difference between the distance from the scatterer involved in the last hop of the path to the nth receiving antenna and the distance to the receiving reference antenna, is the p transmitted by the mth transmitting antenna Tx Polarization-based wave response, is the p received by the nth receiving antenna Rx Polarization-wise wave arrival response, Ω Tx,m,l is the wave departure direction of the mth transmitting antenna on the lth path, Ω Rx,n,l is the direction of arrival of the nth receiving antenna on the lth path, ω i,n,m (t) is the complex symmetric Gaussian noise component, and T is the duration of the measurement.

4. The imaging method based on neighborhood search and continuous interference elimination according to claim 1, characterized in that: In step S3, at the position of the scatterer, based on the half-wavelength constraint of Abbe imaging theory, a uniform diffusion method is used to generate an imaging plane.

5. The imaging method based on neighborhood search and continuous interference elimination according to claim 1, characterized in that: In step S4, based on neighborhood search, spatial sub-pixel points are uniformly generated in the neighborhood of each spatial pixel point, and the time delay and phase difference from the receiving antenna to each spatial sub-pixel point are calculated based on the known Cartesian coordinates of the receiving antenna and the spatial sub-pixel point.

6. The imaging method based on neighborhood search and continuous interference elimination according to claim 1, characterized in that: In step S5, the amplitude of the spatial sub-pixel point is estimated according to the maximum likelihood principle through the response and phase difference information received by the antenna.

7. The imaging method based on neighborhood search and continuous interference elimination according to claim 1, characterized in that: In step S9, the amplitude is normalized by mapping to the interval [0, 1] using linear normalization.

8. The imaging method based on neighborhood search and continuous interference elimination according to claim 1, characterized in that: The interval k of the spatial sub-pixel points is: Where: λ is the wavelength of the radio wave, N interp Generates the number of sub-pixels on one side of a pixel's neighborhood.

9. An imaging device based on neighborhood search and continuous interference elimination, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 8 is implemented.

Images