An imaging method based on neighborhood search and successive interference cancellation

Abstract

The application relates to an imaging method based on neighborhood search and continuous interference cancellation, comprising the following steps: S1, acquiring channel frequency domain response data of a wireless channel; S2, determining the position of a scatterer in an environment; S3, generating an image plane and setting a spatial pixel point; S4, generating a plurality of spatial sub-pixel points in the neighborhood of each spatial pixel point, and time delay and phase difference; S5, calculating a reconstruction signal according to phase difference information; S6, selecting an imaging result point in the neighborhood of each spatial pixel point; S7, removing the reconstruction signal of the imaging result point from complete channel response data; S8, repeating steps S4 to S7 for each spatial pixel point neighborhood in space; and S9, setting normalized brightness information with a corresponding amplitude modulus size at the spatial position of each imaging result point, so as to realize spatial imaging. Compared with the prior art, the application has the advantages of strong anti-interference capability, high imaging contrast and good compatibility for target shapes.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless communication and communication sensing integration, and particularly to an imaging method based on neighborhood search and successive interference cancellation. BACKGROUND

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

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

[0004] However, at the present stage, in the face of complex environment and challenging intelligent applications, the communication sensing integration system using channel response to realize sensing imaging is faced with the problems of low parameter estimation resolution, not clear enough imaging edges, and not high enough imaging contrast. This makes it difficult to realize accurate imaging of small targets in the environment. In view of this problem, it is urgent to improve the existing imaging algorithm to improve the accuracy and contrast of imaging, and suppress background noise, so as to realize more accurate target imaging and environmental sensing. SUMMARY

[0005] The purpose of the present application is to provide an imaging method based on neighborhood search and successive interference cancellation.

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

[0007] An imaging method based on neighborhood search and successive interference cancellation, comprising:

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

[0009] Step S2: estimating the parameters of multipath components based on the channel frequency domain response data, and determining the positions of scatterers in the environment;

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

[0011] Step S4: Based on the neighborhood search, a plurality of spatial sub-pixel points are generated in the neighborhood of each spatial pixel point, and the time delay and phase difference of 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, the electric wave amplitude at each spatial sub-pixel point is estimated based on the maximum likelihood principle, and the reconstructed signal is calculated;

[0013] Step S6: In the neighborhood of each spatial pixel point, the spatial sub-pixel point position and the corresponding amplitude with the smallest mean square error of the original signal are selected as the imaging result point of the corresponding region of the spatial pixel point;

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

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

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

[0017] The step S2 comprises:

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

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

[0020] Step S2-3: Based on the multipath parameters, the positions of scatterers in the environment are determined in combination with the parameterized channel model.

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

[0022]

[0023] Wherein: y i,n,m (t) is the signal transmitted by the mth transmitting antenna and received by the nth receiving antenna in the ith 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, the value range is p Tx ,p Rx ∈{1,2}, representing two mutually orthogonal linear polarization directions, v lThe Doppler frequency of the lth path connecting the transmitting reference antenna and the receiving reference antenna, t i,n,m The channel measurement time of the mth transmitting antenna to the nth receiving antenna in the ith measurement period, τ l The time delay of the lth path connecting the transmitting reference antenna and the receiving reference antenna, Δτ Tx,m,l The time 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 from the scatterer to the transmitting reference antenna, Δτ Rx,n,l The time 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 from the scatterer to the receiving reference antenna, The p Tx The polarization mode wave departure response, The p Rx The polarization mode wave arrival response, Ω Tx,m,l The wave departure direction of the mth transmitting antenna on the lth path, Ω Rx,n,l The wave arrival direction of the nth receiving antenna on the lth path, ω i,n,m (t) is a complex symmetric Gaussian noise component, and T is the time duration of the measurement.

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

[0025] In the 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 of the receiving antenna to each spatial sub-pixel point are calculated according to the known Cartesian coordinates of the receiving antenna and the spatial sub-pixel points.

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

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

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

[0029]

[0030] Where: λ is the wavelength of the electric wave, N interp The number of single-side sub-pixel points generated in the neighborhood of a pixel point.

[0031] An imaging device based on neighborhood search and successive interference cancellation, comprising a memory, a processor, and a program stored in the memory, the processor implementing the method as described above when executing the program.

[0032] A storage medium having a program stored thereon, the program implementing the method of any one of claims 1-8 when executed.

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

[0034] 1. By using channel characteristic information, neighborhood search is performed in the manner of generating sub-pixel points, local optimal solution under regional spatial constraints is ensured, and the amplitudes of multiple signals are estimated based on the maximum likelihood principle between multiple pixel point regions, and crosstalk is reduced by updating hidden data.

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

[0036] 3. According to the successive interference cancellation principle, the reconstructed signal of the imaging result point in the region corresponding to the spatial pixel point is removed from the complete channel response data, it is ensured that the reconstructed signal does not affect subsequent estimation, crosstalk between pixel points can be more effectively suppressed, and better imaging effect can be achieved. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 It is a main step flowchart of the method of the present application;

[0038] Figure 2 It is a spatial scatterer distribution schematic diagram in the embodiment;

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

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

[0041] Figure 5 It is a sensing imaging result in the embodiment using the neighborhood search and successive interference cancellation based on the present application. DETAILED DESCRIPTION

[0042] The present application will be described in detail below in combination with the drawings and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present application, and detailed implementation and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0043] An imaging method based on neighborhood search and successive interference cancellation, as follows:Figure 1 As shown, comprising:

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

[0045] In this embodiment, the acquisition of the channel frequency domain response data can be achieved by propagation graph theory simulation or measurement using a virtual network analyzer in a real scene.

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

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

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

[0049]

[0050] Wherein: y i,n,m (t) is a signal transmitted by the mthtransmitting antenna and received by the nthreceiving antenna in the ithmeasurement 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 the value range is p Tx ,p Rx ∈{1,2}, representing two mutually orthogonal linear polarization directions, v l is the Doppler frequency of the lthpath connecting the transmitting reference antenna and the receiving reference antenna, t i,n,m is the channel measurement time of the mthtransmitting antenna to the nthreceiving antenna in the ithmeasurement period, τ l is the time delay of the lthpath connecting the transmitting reference antenna and the receiving reference antenna, Δτ Tx,m,l is the time delay difference caused by the difference between the distance from the scatterer involved in the first hop of the path to the mthtransmitting antenna and the distance to the transmitting reference antenna, Δτ Rx,n,l is the time delay difference caused by the difference between the distance from the scatterer involved in the last hop of the path to the nthreceiving antenna and the distance to the receiving reference antenna, is the wave off response of the p Tx polarization mode transmitted by the mthtransmitting antenna, is the wave arrival response of the p Rx polarization mode received by the nthreceiving antenna, Ω Tx,m,l is the wave off direction of the mthtransmitting antenna on the lthpath, Ω Rx,n,l is the wave arrival direction of the nthreceiving antenna on the lthpath, ω i,n,m (t) is a complex symmetric Gaussian noise component, and T is the time duration of the measurement.

[0051] where Ω represents a unit direction vector, which is uniquely determined by its azimuth angle φ and elevation angle θ, and its expression is:

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

[0053] Generally, the positions of the transmitting reference antenna and the receiving reference antenna are coincident with the coordinate system origins of the transmitting end and the receiving end, respectively. Using these symbols, the DoD (Ω Tx,m,l ), the DoA (Ω Rx,n,l ), and the time delay can be expressed as:

[0054]

[0055] where represents the position of the transmitting reference antenna, represents the position of the receiving reference antenna, d Tx,l represents the distance from the transmitting reference antenna to the scatterer involved in the first hop along the lth path, d Rx,l represents the distance from the receiving reference antenna to the scatterer involved in the last hop along the lth path, 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 parameter, and c represents the speed of light. It can be proved that when and , there are and that is, when d Tx,l and d Rx,l are greater than d Rayleigh , the spherical wave front can be approximated as a plane wave front. Compared with the plane wave model, the spherical wave model is more in line with the characteristics of the electric wave itself and is more conducive to expressing the non-stationary characteristics of large array scenarios. When the size of the antenna array is relatively small, is of the same order of magnitude as , and similarly, is of the same order of magnitude as , which indicates that the DoD of the transmitting antenna and the DoA of the receiving antenna are no longer affected by the positions of the array elements in the antenna array, i.e., the propagation characteristics of the electric wave are the same as those of a plane wave. The spherical wave model is a universal channel model, and the plane wave model is a simplified case under a small antenna array size. The spherical wave model is used as the channel model in the present application.

[0056] Step S2-2: using a high-precision parameter estimation algorithm to estimate the 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 L multipaths. In the single-input single-output (SIMO) scenario, the model can be simplified, and the unknown parameters to be estimated are:

[0058] Θ = [θ1, …, θ l , …, θ L ].

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

[0060]

[0061] wherein z(·) represents a likelihood function, represents the time delay estimation value of the lth path, represents the horizontal angle estimation value of the lth path to the Rx array, represents the elevation angle estimation value of the lth path to the Rx array. Through continuous iteration, the parameter value can be estimated. Through geometric relationship, the position s Rx,l of the scatterer involved in the last hop of the lth path is expressed as:

[0062]

[0063] Step S2-3: Based on the multipath parameters, the positions of the scatterers in the environment are determined in combination with the parameterized channel model.

[0064] Step S3: An image plane is generated at the position of the scatterer, and a spatial pixel point is set;

[0065] wherein the generation of the spatial pixel point is based on the center of the imaging plane, and the imaging plane size and the initial step length are set. The initial step length is set to half a wavelength, so that the signals between the adjacent pixel points can realize continuous interference cancellation.

[0066] Step S4: Based on the neighborhood search, a plurality of spatial sub-pixel points are generated in the neighborhood of each spatial pixel point, and the time delay and phase difference of the receiving antenna to each spatial sub-pixel point are calculated according to the geometric information, wherein the specific method is: 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 of the receiving antenna to each spatial sub-pixel point are calculated according to the known Cartesian coordinates of the receiving antenna and the spatial sub-pixel points.

[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 , uniformly generate the corresponding one-dimensional coordinates and combine them 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 a single edge within 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 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 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: Based on the phase difference information and the maximum likelihood principle, the amplitude of the radio wave at each spatial sub-pixel point is estimated to calculate the reconstructed signal.

[0075] Specifically, the maximum likelihood principle is used to calculate the amplitude of spatial sub-pixels based on the phase difference. Q sub-pixels are generated in the neighborhood of the p-th initial pixel, and the phase rotation vector of the q-th sub-pixel around the p-th initial pixel 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 where N is the number of Rx antennas and K is the number of frequency points. The likelihood function can be expressed as:

[0080]

[0081] Let the partial derivative of Λ(s p ; x p ) with respect to s be 0, the complex amplitude of the qth sub-pixel point around the pth initial pixel point can be obtained as:

[0082]

[0083] The amplitude estimation 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 is updated, that is, the reconstructed signal component is removed from the implicit data so as to not affect the subsequent estimation. The updating process of the implicit data is:

[0086]

[0087] Step S6: In the neighborhood of each spatial pixel point, the spatial sub-pixel point position and the corresponding amplitude with the minimum original signal mean square error are selected as the imaging result point of the corresponding region of the spatial pixel point.

[0088] Step S7: According to the successive interference cancellation principle, the reconstructed signal of the imaging result point in the region corresponding to the spatial pixel point is removed from the complete channel response data.

[0089] Step S8: Steps S4 to S7 are repeated for each spatial pixel point neighborhood in space until the traversal of all spatial pixel points is completed.

[0090] Step S9: At each spatial position of the imaging result point, normalized brightness information with a corresponding amplitude modulus size is set to realize spatial imaging. According to the local optimal estimation of the estimated pixel point spatial position and the corresponding amplitude, the amplitude is linearly normalized to the [0, 1] interval, and the normalized amplitude is placed at the corresponding spatial position, so that the target object imaging can be realized.

[0091] The following describes an experimental process.

[0092] Figure 2 ​The spatial scatterer distribution diagram shown, according to the transceiver in the scene, and the geometric position relationship of the scatterer distribution, it can be known that the path in the environment is a line of sight (LOS), and the scene is a SIMO scene. The imaging mode is a reflection type imaging, the transmitting end antenna is a single antenna, the spatial coordinates are, the scatterer plane is parallel to the YOZ plane, the X-axis coordinate is 0.1 m, the receiving end antenna is a 101*31 antenna array, and the array is parallel to the YOZ plane and takes the origin as the center. The shape of the scatterer in the space is three equidistant rectangles distributed along the Y-axis direction, the length is 0.01 m, the width is 0.005 m, the interval is 0.01 m, and the geometric center is. The frequency of the electromagnetic wave is GHz, the frequency point number is 101, the carrier frequency is 140 GHz, and the bandwidth is 4 GHz. Through Figure 2 The setting of the digital map, based on the synthesis channel simulation, the amplitude of the scatterer is set, and then the signal is synthesized according to the spatial phase information, so that the CIR of the synthesis channel can be synthesized, the simulation of the channel response is realized, and the power-delay profile (PDP) of the simulation is as shown in Figure 3 .

[0093] For the above case, the specific implementation process of the algorithm in the application is as shown in Figure 1 The method generates spatial sub-pixel points, realizes neighborhood search based on the least square error principle, removes the reconstruction signal of the area imaging result points in the complete channel response data according to the SIC method, suppresses background noise, realizes more fine imaging, and specifically as follows:

[0094] Firstly, according to 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 maximum SAGE algorithm is used to realize high-precision estimation of the parameters. The estimation order of each parameter and the iterative update formula are as follows:

[0095]

[0096] Wherein, z(·) represents a likelihood function, z (l) represents the time delay estimation value of the lth path, z (l) represents the horizontal angle estimation value of the lth path to the Rx array, z (l) represents the elevation angle estimation value of the lth path to the Rx array. After continuous iteration, the parameter value can be estimated. Since the amplitude of the electric wave at the scatterer position is directly set in the scene, the time delay estimated by the SAGE algorithm is actually the time delay of the scatterer to the Rx array. Through the geometric relationship, the position s Rx,l of the scatterer involved in the last hop of the lth path is expressed as:

[0097]

[0098] By setting the path number as 3 and the iteration round as 3, the angle-power spectrum of the scatterer distribution in the space can be obtained as shown in FIG. 6. Figure 4 Figure 4 The main distribution of the equivalent scatterer position can be seen in FIG. 6, and the spatial position parameter of the main path is estimated as [0.28 ns, 0°, 90°]. The main path position estimated by the parameter can be converted into the Cartesian coordinate system through the spherical coordinate system, and the main path position estimated by the parameter is [0.085, 0, 0] in the Cartesian coordinate system.

[0099] Then, at the estimated position, an imaging plane with a size of 60x20 is generated parallel to the Rx array, and the pixel point interval is half of the wavelength, and the imaging range is a rectangular area with a size of 0.0643x0.0214. For each generated pixel point, a neighborhood search method is used to uniformly generate a plurality of spatial sub-pixel points in a neighborhood of a pixel point, and the sub-pixel point interval is set to 1 / 3 of the neighborhood interval of the pixel point, that is, 9 spatial sub-pixel points will be uniformly generated in a neighborhood of a pixel point. The phase at the spatial position of the generated sub-pixel point is calculated, and the amplitude at the spatial sub-pixel point is estimated according to the maximum likelihood principle. The carrier frequency is f, the coordinate of the pth neighborhood spatial sub-pixel point is r p,q , the spatial coordinate of the nth receiving antenna is r Rx,n , and the phase difference s p,q,n between the pth neighborhood spatial sub-pixel point and the nth receiving antenna can be expressed as:

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

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

[0102]

[0103] Wherein, can be calculated through 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] ​where α p,q The implicit data of the complete data y is estimated, N is the number of Rx antennas, and K is the number of frequency points. w obeys Gaussian distribution, and the likelihood function can be expressed as:

[0106]

[0107] Let Λ(s p ; x p ) be the partial derivative of to 0, and the complex amplitude of the qth sub-pixel point around the pth initial pixel point can be obtained as:

[0108]

[0109] The amplitude estimation 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 amplitude of the sub-pixel points in the spatial neighborhood, a sub-pixel point with the minimum mean square error in each neighborhood is selected as the imaging result point in the neighborhood.

[0112] Finally, after completing the amplitude estimation, the implicit data is updated, that is, the reconstructed signal component is removed from the implicit data so as to not affect the subsequent estimation. The updating process of the implicit data is:

[0113]

[0114] According to the local optimal estimation of the spatial position and corresponding amplitude of the pixel point obtained by estimation, the amplitude is linearly normalized to the interval [0, 1], and the normalized amplitude is placed at the corresponding spatial position, so that the target object imaging is realized. The final imaging result of the simulation object is shown in Figure 5 .

[0115] ​If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for causing 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 methods described in the various embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various media that can store program codes.

Claims

1. An imaging method based on neighborhood search and successive interference cancellation, characterized in that, The method comprises the following steps: Step S1: acquiring channel frequency domain response data of a wireless channel; Step S2: estimating multipath component parameters based on the channel frequency domain response data, and determining positions of scatterers in the environment; Step S3: generating an image plane at the positions of the scatterers, and setting spatial pixel points; Step S4: generating a plurality of spatial sub-pixel points in a neighborhood of each spatial pixel point based on neighborhood search, and calculating time delay and phase difference of a receiving antenna to each spatial sub-pixel point according to geometric information; Step S5: estimating an electric wave amplitude at each spatial sub-pixel point based on a maximum likelihood principle according to phase difference information, and calculating a reconstructed signal; Step S6: selecting a spatial sub-pixel point position and a corresponding amplitude with a minimum mean square error with an original signal in a neighborhood of each spatial pixel point as an imaging result point of a region corresponding to the spatial pixel point; Step S7: removing the reconstructed signal of the imaging result point in the region corresponding to the spatial pixel point from complete channel response data according to a successive interference cancellation principle; Step S8: repeating steps S4 to S7 for each spatial pixel point neighborhood in the space until traversal of all spatial pixel points is completed; Step S9: setting normalized brightness information of a modulus size of a corresponding amplitude at a spatial position of each imaging result point to realize spatial imaging.

2. The imaging method based on the neighborhood search and successive interference cancellation according to claim 1, characterized in that, The step S2 comprises: Step S2-1: selecting a parameterized channel model; Step S2-2: estimating multipath parameters based on the channel frequency domain response data using a high-precision parameter estimation algorithm; Step S2-3: determining positions of scatterers in the environment based on the multipath parameters and the parameterized channel model.

3. The imaging method based on a neighborhood search and successive interference cancellation according to claim 2, characterized in that, The parameterized channel model is a spherical wave model, and a mathematical expression is: wherein: y i,n,m (t) is the signal transmitted by the mthtransmitting antenna and received by the nthreceiving antenna in the ithmeasurement 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 p Tx Rx ∈{1,2}, represents two mutually orthogonal linear polarization directions, v l is the Doppler frequency of the lthpath connecting the transmitting reference antenna and the receiving reference antenna, t i,n,m is the channel measurement time of the mthtransmitting antenna to the nthreceiving antenna in the ithmeasurement period, τ l is the time delay of the lthpath connecting the transmitting reference antenna and the receiving reference antenna, Δτ Tx,m,l is the time delay difference caused by the difference between the distance of the scatterer involved by the first hop of the path to the mthtransmitting antenna and the distance to the transmitting reference antenna, Δτ Rx,n,l is the time delay difference caused by the difference between the distance of the scatterer involved by the last hop of the path to the nthreceiving antenna and the distance to the receiving reference antenna, is the wave departure response of the p Tx polarization mode of the mthtransmitting antenna, is the wave arrival response of the p Rx polarization mode of the nthreceiving antenna, Ω Tx,m,l is the wave departure direction of the mthtransmitting antenna on the lthpath, Ω Rx,n,l is the wave arrival direction of the nthreceiving antenna on the lthpath, ω i,n,m (t) is a complex symmetric Gaussian noise component, and T is the time duration of the measurement.​ 4. The imaging method based on the neighborhood search and successive interference cancellation according to claim 1, characterized in that, In the step S3, the imaging plane is generated in a uniform diffusion manner based on a half-wavelength constraint of Abbe imaging theory at the positions of the scatterers.

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

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

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

8. The imaging method based on the neighborhood search and successive interference cancellation according to claim 1, characterized in that, The interval k of the spatial sub-pixel points is: where: λ is the wavelength of the electric wave, N interp is the number of single side sub-pixel points in the neighborhood of a pixel point.

9. An imaging apparatus based on a neighborhood search and successive interference cancellation, comprising a memory, a processor, and a program stored in the memory, wherein, The processor implements the method according to any one of claims 1-8 when executing the program.

10. A storage medium having stored thereon a program, characterized by The program is executed to implement the method according to any one of claims 1-8.

Images