Near-field imaging method of sparse MIMO array cylindrical scanning SAR system

Abstract

The invention relates to the technical field of millimeter wave near-field imaging, in particular to a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system, and the method comprises the steps: dividing a complete comprehensive array of the SAR system into a plurality of sub-arrays, selecting local echo data corresponding to the plurality of sub-arrays from complete echo data, and calculating the local echo data of each first-stage sub-array in (u, v, v); according to the method, a uniform sampling grid with a uniform sampling rate in an (x, y, z) coordinate system is obtained, the uniform sampling grid is converted into a first-level non-uniform sampling grid in the (x, y, z) coordinate system, a first-level sub-image is reconstructed in the first-level non-uniform sampling grid based on local echo data, and a pairwise coherent superposition process is iteratively executed; and obtaining a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system. According to the invention, the calculation complexity of imaging can be effectively reduced, and accurate and efficient near-field three-dimensional imaging of an arbitrary linear sparse MIMO array cylindrical scanning SAR system is realized.

Description

Technical Field

[0001] The present application relates to the field of millimeter wave near-field imaging technology, and in particular to a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system. Background Art

[0002] Millimeter-wave imaging technology based on near-field active arrays can achieve high spatial resolution while implementing large apertures and wide operating bandwidths. Combined with the non-ionizing nature and penetrability of millimeter waves, this technology has been widely researched and applied in various fields, including security inspections, medical diagnostics, through-wall imaging, and non-destructive testing.

[0003] In applications such as security inspections, 3D millimeter-wave imaging systems require not only high performance but also all-around illumination of the imaging target, reducing blind spots and being able to acquire target images over a wider field of view. Cylindrical scanning SAR systems have become a competitive solution due to their multi-angle observation advantages and have received extensive research attention. Compared with traditional planar scanning SAR systems, cylindrical scanning SAR systems rotate and scan at a fixed radius to form a cylindrical synthetic aperture, which can achieve all-around illumination of the target. The sparse MIMO array can further reduce the number of physical array elements and actual channels to achieve shorter data acquisition time. The sparsity of the array can effectively reduce sidelobe interference, provide richer spectral information and a higher dynamic range, and improve imaging quality.

[0004] However, the related art still lacks accurate and efficient three-dimensional imaging methods for linear sparse MIMO array cylindrical scanning SAR systems. Many application scenarios have extremely high requirements for imaging speed and quality, and there is an urgent need to improve imaging efficiency while ensuring high-quality imaging. Existing traditional imaging methods, such as the back-projection method (BPA) and Kirchhoff migration method (KMA) in the time domain, can achieve high-precision image reconstruction by effectively compensating for trajectory deviations at each scanning position and can be directly applied to non-uniform integrated arrays. However, their actual implementation is too computationally complex, hindering their application in real-time imaging systems. Most spatial wavenumber domain imaging methods are designed for uniform arrays. Although research literature has proposed methods suitable for non-uniform sparse arrays, such as the non-uniform fast Fourier transform-based range migration method (NUFFT-based-RMA), their imaging quality and computational efficiency are not high when the array sparsity increases, which urgently needs to be addressed. Summary of the Invention

[0005] The present application provides a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system to address the problems of traditional imaging methods and range migration methods based on non-uniform fast Fourier transform in related technologies, which still need to be improved in imaging speed, and with the increase of array sparsity, the imaging quality and computational efficiency also have certain limitations.

[0006] A first aspect embodiment of the present application provides a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system, comprising the following steps: acquiring complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system, and dividing the complete integrated array into multiple subarrays to select local echo data corresponding to the multiple subarrays from the complete echo data; reconstructing a subimage corresponding to each subarray in a non-uniform sampling grid corresponding to each subarray in the multiple subarrays based on the local echo data; and superimposing the subimage of each subarray according to a preset superposition rule to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system.

[0007] Optionally, in one embodiment of the present application, obtaining complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system includes: obtaining original echo data of a complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system; correcting the original echo data based on amplitude inconsistency, time delay inconsistency and amplitude imbalance between multiple channel signals at different frequencies in the target linear sparse MIMO array cylindrical scanning SAR system to obtain the complete echo data.

[0008] Optionally, in one embodiment of the present application, before reconstructing the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each sub-array in the multiple sub-arrays, the method further includes: simulating the spectral characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system based on the local spectrum analysis results of the target linear sparse MIMO array cylindrical scanning SAR system to analyze the local spectrum support domain of the image at any position; analyzing the approximate minimum volume bounding box of the local spectrum support domain to convert the actual bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain of the target linear sparse MIMO array cylindrical scanning SAR system to obtain a restricted support range of the local spectrum; and calculating, based on the restricted support range, a uniform sampling grid with a uniform sampling rate for each sub-array in the (u, v, n) coordinate system to convert the uniform sampling grid into a non-uniform sampling grid in the (x, y, z) coordinate system.

[0009] Optionally, in one embodiment of the present application, the calculation formula for the uniform sampling grid of each subarray with a uniform sampling rate in the (u, v, n) coordinate system is:

[0010]

[0011]

[0012] Among them, u(x,y,z), v(x,y,z), and n(x,y,z) represent the transformed (u,v,n) coordinates respectively; k max 、k min Represent the maximum and minimum values ​​of the wave number respectively; x0, y0, z0 are the parameters in (x0, y0, z0), (x0, y0, z0) represents the slowly changing stationary point; R represents the scanning radius of the system; z t,ζ,SA With z r,ζ,SA Respectively represent the transmitting and receiving antennas closest to z0 in the transmitting and receiving subarrays, z t,min,SA 、z t,max,SA 、z r,min,SA 、z r,max,SA Respectively represent the minimum and maximum values ​​of the z coordinate of the receiving subarray and the minimum and maximum values ​​of the z coordinate of the transmitting subarray; A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process; θ ζ,SA The subarray scanning angle corresponding to the minimum distance difference between the target point and the projection point of each antenna on the xoy plane is θ; min,SA and θ max,SA represents the minimum scanning angle and maximum scanning angle of the subarray, θ mid,SA is the midpoint of the subarray scan angle.

[0013] Optionally, in one embodiment of the present application, superimposing the sub-images of each sub-array according to a preset superposition rule to obtain three-dimensional imaging of the target linear sparse MIMO array cylindrical scanning SAR system includes: performing space-variant spatial down-conversion processing on the sub-images of each sub-array to obtain compressed sub-images; interpolating the compressed sub-images onto an adjacent high-level sampling grid at a corresponding level of each sub-array to perform pairwise coherent superposition on the sub-images of each sub-array to obtain multiple sub-image pairs, and repeating the pairwise coherent superposition until a unique sub-image pair is obtained; and determining the three-dimensional imaging result based on the unique sub-image pair.

[0014] Optionally, in one embodiment of the present application, the expression for the space-variable spatial down-conversion processing is:

[0015] f m,n '(p)=f m,n (p)e -jΦ

[0016]

[0017] Among them, f m,n '(p) represents the reconstruction result of the m-th level sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th level sub-image, j represents the complex exponential term; Φ is a function of different positions (x, y, z) in space and the frequency of the transmitted signal, k max 、k min They represent the maximum and minimum values ​​of the wave number respectively. A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process.

[0018] A second aspect of the present application provides a near-field imaging device for a sparse MIMO array cylindrical scanning SAR system, comprising: an acquisition module for acquiring complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system, and dividing the complete integrated array into multiple subarrays to select local echo data corresponding to the multiple subarrays from the complete echo data; a reconstruction module for reconstructing a sub-image corresponding to each subarray in a non-uniform sampling grid corresponding to each subarray in the multiple subarrays based on the local echo data; and an imaging module for superimposing the subimage of each subarray according to a preset superposition rule to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system.

[0019] Optionally, in one embodiment of the present application, the acquisition module includes: an acquisition unit for acquiring the original echo data of the complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system; a correction unit for correcting the original echo data based on the amplitude inconsistency, delay inconsistency and amplitude imbalance between multiple channel signals in the target linear sparse MIMO array cylindrical scanning SAR system at different frequencies to obtain the complete echo data.

[0020] Optionally, in one embodiment of the present application, it further includes: a simulation module, which is used to simulate the spectral characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system based on the local spectrum analysis results of the target linear sparse MIMO array cylindrical scanning SAR system before reconstructing the sub-image corresponding to each subarray in the non-uniform sampling grid corresponding to each subarray of the multiple subarrays, so as to analyze the local spectrum support domain of the image at any position; an analysis module, which is used to analyze the approximate minimum volume bounding box of the local spectrum support domain, so as to convert the actual bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain of the target linear sparse MIMO array cylindrical scanning SAR system, and obtain the restricted support range of the local spectrum; and a calculation module, which is used to calculate a uniform sampling grid with a uniform sampling rate for each subarray in the (u, v, n) coordinate system, so as to convert the uniform sampling grid into a non-uniform sampling grid in the (x, y, z) coordinate system.

[0021] Optionally, in one embodiment of the present application, a calculation formula for a uniform sampling grid with a uniform sampling rate for each subarray in the (u, v, n) coordinate system can be expressed as:

[0022]

[0023] Among them, u(x,y,z), v(x,y,z), and n(x,y,z) represent the transformed (u,v,n) coordinates respectively; k max 、k min Represent the maximum and minimum values ​​of the wave number respectively; x0, y0, z0 are the parameters in (x0, y0, z0), (x0, y0, z0) represents the slowly changing stationary point; R represents the scanning radius of the system; z t,ζ,SA With z r,ζ,SA Respectively represent the transmitting and receiving antennas closest to z0 in the transmitting and receiving subarrays, z t,min,SA 、z t,max,SA 、z r,min,SA 、z r,max,SA Respectively represent the minimum and maximum values ​​of the z coordinate of the receiving subarray and the minimum and maximum values ​​of the z coordinate of the transmitting subarray; A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process; θ ζ,SA The subarray scanning angle corresponding to the minimum distance difference between the target point and the projection point of each antenna on the xoy plane is θ; min,SA and θ max,SA represents the minimum scanning angle and maximum scanning angle of the subarray, θ mid,SA is the midpoint of the subarray scan angle.

[0024] Optionally, in one embodiment of the present application, the imaging module includes: a processing unit, configured to perform space-variant spatial down-conversion processing on the sub-image of each sub-array to obtain a compressed sub-image; a superposition unit, configured to interpolate the compressed sub-image onto an adjacent higher-level sampling grid corresponding to the level of each sub-array, so as to perform pairwise coherent superposition on the sub-images of each sub-array to obtain multiple sub-image pairs, and repeat the pairwise coherent superposition until a unique sub-image pair is obtained; and a determination unit, configured to determine the three-dimensional imaging result based on the unique sub-image pair.

[0025] Optionally, in one embodiment of the present application, the expression for the space-variable spatial down-conversion processing is:

[0026] f m,n '(p)=f m,n (p)e -jΦ

[0027]

[0028] Among them, f m,n '(p) represents the reconstruction result of the m-th level sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th level sub-image, j represents the complex exponential term; Φ is a function of different positions (x, y, z) in space and the frequency of the transmitted signal, k max 、k min They represent the maximum and minimum values ​​of the wave number respectively. A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process.

[0029] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system as described in the above embodiment.

[0030] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system as described above.

[0031] A fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned near-field imaging method of the sparse MIMO array cylindrical scanning SAR system.

[0032] The embodiment of the present application can divide the complete integrated array into multiple sub-arrays, reconstruct the downsampled sub-image of each sub-array, and then obtain the near-field three-dimensional imaging of the target linear sparse MIMO array cylindrical scanning SAR system by superimposing the sub-images. Thus, by analyzing the local spectrum characteristics, the analytical expressions of SDC and LLT are innovatively derived, and the spectrum of the sub-image is compressed using them, thereby reducing the number of sampling points and overcoming the problems of high time complexity and computational complexity of traditional time domain imaging methods; then the time domain imaging method is used to reconstruct the downsampled sub-image of each sub-array, and the sub-images of subsequent levels are obtained by merging two by two, and finally an accurate three-dimensional reconstruction result is obtained, realizing accurate and efficient three-dimensional imaging of any linear sparse MIMO array cylindrical scanning SAR system. Thus, the traditional imaging method and the range migration method based on non-uniform fast Fourier transform in the related art are solved, which still need to be improved in imaging speed, and with the increase of array sparsity, the imaging quality and computational efficiency also have certain limitations.

[0033] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0035] Figure 1 This is a flowchart of a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system according to an embodiment of the present application;

[0036] Figure 2 This is a schematic diagram of the geometric structure of a linear sparse MIMO array cylindrical scanning SAR near-field imaging system according to one embodiment of the present application;

[0037] Figure 3 Schematic diagram of the convex hull, key points, and minimum volume bounding box of the sub-image local spectrum support domain under different linear sparse MIMO arrays in one embodiment of the present application;

[0038] Figure 4 This is a schematic diagram of the support domain of a sub-image spectrum after SDC and LLT transformation in one embodiment of the present application;

[0039] Figure 5 This is a flowchart of a three-dimensional fast near-field imaging method applicable to a linear sparse MIMO array cylindrical scanning SAR system according to one embodiment of the present application;

[0040] Figure 6 This is a schematic diagram comparing three-dimensional imaging results according to an embodiment of the present application;

[0041] Figure 7 A schematic structural diagram of a near-field imaging device for a sparse MIMO array cylindrical scanning SAR system according to an embodiment of the present application;

[0042] Figure 8 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application.

[0043] Reference numerals:

[0044] 10-Near-field imaging device of sparse MIMO array cylindrical scanning SAR system: 100-acquisition module, 200-reconstruction module and 300-imaging module; 801-memory, 802-processor and 803-communication interface. DETAILED DESCRIPTION

[0045] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0046] The following describes a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system according to an embodiment of the present application with reference to the accompanying drawings. With respect to the conventional imaging methods and the range migration method based on non-uniform fast Fourier transform in the related technologies mentioned in the above background technology, the imaging speed still needs to be improved, and as the array sparsity increases, the imaging quality and computational efficiency also have certain limitations. The present application provides a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system, in which the complete integrated array can be divided into multiple sub-arrays, a downsampled sub-image of each sub-array can be reconstructed, and then the near-field three-dimensional imaging of the target linear sparse MIMO array cylindrical scanning SAR system can be obtained by superimposing the sub-images. This method innovatively derives analytical expressions for SDC and LLT by analyzing local spectral characteristics, and uses them to perform sub-image spectral compression, thereby reducing the number of sampling points and overcoming the high time and computational complexity of traditional time-domain imaging methods. In implementation, the method uses time-domain imaging to reconstruct downsampled sub-images of each subarray, and then obtains sub-images of subsequent layers by pairwise merging, ultimately achieving accurate three-dimensional reconstruction results and achieving accurate and efficient three-dimensional imaging for any linearly sparse MIMO array cylindrical scanning SAR system. This addresses the issues of conventional imaging methods and range migration methods based on non-uniform fast Fourier transforms in related technologies, which still require improvement in imaging speed and have certain limitations in imaging quality and computational efficiency as array sparsity increases.

[0047] Before explaining the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system in the embodiment of the present application, to facilitate those skilled in the art to understand the present application, Table 1 is a formula parameter table of one embodiment of the present application. Table 1 is used to explain the parameter symbols involved in the present application. Table 1 can be, but is not limited to, represented as follows:

[0048]

[0049]

[0050] Based on this parameter table, the present application can construct a certain formula to represent the geometric structure of the sparse MIMO array cylindrical scanning SAR system to analyze the spectral characteristics of the SAR image, give the derivation process and results of LLT and SDC, and provide theoretical support for the optimal sampling grid design in the embodiments of the present application and reduce the computational load of 3D imaging.

[0051] Specifically, Figure 1 This is a flowchart of a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system provided in an embodiment of the present application.

[0052] like Figure 1 As shown, the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system includes the following steps:

[0053] In step S101, complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system is obtained, and the complete integrated array is divided into multiple sub-arrays to select local echo data corresponding to the multiple sub-arrays from the complete echo data.

[0054] It can be understood that the target linear sparse MIMO array cylindrical scanning SAR system is a synthetic aperture radar (SAR) system that combines sparse array technology, multiple input multiple output (MIMO) radar and cylindrical scanning mode.

[0055] Among them, a sparse array means that the antenna units are non-uniformly distributed on a straight line. By optimizing the array element spacing to reduce redundant sampling, the system complexity and cost are reduced. Compared with the traditional uniform array, the number of array elements can be reduced.

[0056] Cylindrical scanning refers to a cylindrical scanning method, whereby the radar system scans a cylindrical trajectory around the target area (typically by rotating the radar platform around the target, or the target rotating on a rotating stage), providing 360° or wide-angle observation. Combining range (radial) and azimuth (circumferential) signal processing, three-dimensional imaging of the target can be achieved.

[0057] In some embodiments, after obtaining complete echo data of a target linear sparse MIMO array cylindrical scanning SAR system, embodiments of the present application can select local echo data corresponding to multiple subarrays from the complete echo data based on the multiple subarrays into which the complete integrated array is divided, so as to reconstruct a subimage corresponding to each subarray based on the local echo data.

[0058] Optionally, in one embodiment of the present application, obtaining complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system includes: obtaining original echo data of a complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system; correcting the original echo data based on amplitude inconsistency, time delay inconsistency and amplitude imbalance between multiple channel signals at different frequencies in the target linear sparse MIMO array cylindrical scanning SAR system to obtain complete echo data.

[0059] In a sparse MIMO array cylindrical scanning SAR system, the varying characteristics of each antenna element, such as feeder length, amplifier gain, and RF link components (such as filters), lead to varying amplitude attenuation and delay in the signal transmission channels corresponding to each antenna element. Amplitude inconsistencies between multiple channels can lead to deviations in echo energy distribution, while inconsistent delays can disrupt signal phase coherence, ultimately affecting the accuracy of object shape reconstruction, reducing the resolution and contrast of SAR images, and ultimately impacting image quality.

[0060] In some embodiments, when acquiring complete echo data of a target linear sparse MIMO array cylindrical scanning SAR system, the present application needs to process the original echo data of the complete integrated array in the acquired target linear sparse MIMO array cylindrical scanning SAR system, mainly to correct the amplitude inconsistency, delay inconsistency and amplitude imbalance between multiple channel signals in the target linear sparse MIMO array cylindrical scanning SAR system between different frequencies, that is, to perform amplitude weighting and delay adjustment on the original echo data channel by channel and frequency point by frequency point, and the corrected original echo data is complete echo data.

[0061] Among them, the correction method for amplitude inconsistency, delay inconsistency and amplitude imbalance between multiple channel signals at different frequencies can be selected or adjusted by professional and technical personnel in this technical field according to actual conditions. The embodiments of this application are only for illustrative purposes and are not specifically limited.

[0062] The present application can eliminate the influence of the signal by correcting the amplitude and delay inconsistency between multiple channel signals in the target linear sparse MIMO array cylindrical scanning SAR system and the amplitude imbalance between multiple channel signals at different frequencies, effectively improving the resolution of the final SAR three-dimensional imaging and reducing image distortion.

[0063] Step S102 : reconstructing a sub-image corresponding to each sub-array in a non-uniform sampling grid corresponding to each sub-array in the plurality of sub-arrays based on the local echo data.

[0064] In some embodiments, after extracting local echo data corresponding to multiple subarrays from the complete echo data, the present application can reconstruct a sub-image corresponding to each subarray in the non-uniform sampling grid corresponding to each subarray in the multiple subarrays based on these local echo data.

[0065] The sub-image reconstruction method may include, but is not limited to, a BPA (Back Projection Algorithm) method.

[0066] Next, the reconstruction process of the sub-image in the embodiment of the present application is further explained.

[0067] Optionally, in one embodiment of the present application, before reconstructing the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each sub-array in the multiple sub-arrays, the method further includes: simulating the spectral characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system based on the local spectrum analysis results of the target linear sparse MIMO array cylindrical scanning SAR system to analyze the local spectrum support domain of the image at any position; analyzing the approximate minimum volume bounding box of the local spectrum support domain to convert the actual bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain of the target linear sparse MIMO array cylindrical scanning SAR system to obtain a restricted support range of the local spectrum; based on the restricted support range, calculating a uniform sampling grid with a uniform sampling rate for each sub-array in the (u, v, n) coordinate system to convert the uniform sampling grid into a non-uniform sampling grid in the (x, y, z) coordinate system. The calculation formula for the uniform sampling grid with a uniform sampling rate for each sub-array in the (u, v, n) coordinate system can be, but is not limited to, expressed as:

[0068]

[0069]

[0070] Among them, u(x,y,z), v(x,y,z), and n(x,y,z) represent the transformed (u,v,n) coordinates respectively; k max 、k minRepresent the maximum and minimum values ​​of the wave number respectively; x0, y0, z0 are the parameters in (x0, y0, z0), (x0, y0, z0) represents the slowly changing stationary point; R represents the scanning radius of the system; z t,ζ,SA With z r,ζ,SA Respectively represent the transmitting and receiving antennas closest to z0 in the transmitting and receiving subarrays, z t,min,SA 、z t,max,SA 、z r,min,SA 、z r,max,SA Respectively represent the minimum and maximum values ​​of the z coordinate of the receiving subarray and the minimum and maximum values ​​of the z coordinate of the transmitting subarray; A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process; θ ζ,SA The subarray scanning angle corresponding to the minimum distance difference between the target point and the projection point of each antenna on the xoy plane is θ; min,SA and θ max,SA represents the minimum scanning angle and maximum scanning angle of the subarray, θ mid,SA is the midpoint of the subarray scan angle.

[0071] It is understood by those skilled in the art that near-field imaging methods applicable to linear sparse MIMO array cylindrical scanning SAR systems are difficult to achieve simultaneously guaranteed imaging accuracy and computational efficiency. Therefore, the embodiments of the present application, taking into account the local spectral characteristics of short-range radar images, utilize spatial down-conversion (SDC) and local linear transform (LLT) of linear MIMO array cylindrical scanning SAR systems to achieve spectral compression, then use time-domain imaging methods to reconstruct downsampled sub-images of each subframe, and then generate a complete SAR three-dimensional image from the sub-images.

[0072] As a possible implementation method, the present application can first analyze the spectral support range characteristics (spectral characteristics) of SAR images based on the geometric structure of a linear sparse MIMO array cylindrical scanning SAR imaging system. By analyzing the shape of the wavenumber domain spectrum at different locations in space (the effective coverage range of the spectrum in the wavenumber domain), the space-varying characteristics of the SAR image spectrum can be analytically expressed. In other words, a mathematical analytical expression is used to describe how the shape, range, and boundary contour of the wavenumber domain spectrum support area of ​​the SAR system changes with spatial position (such as the target position or radar platform position) during the imaging process.

[0073] Figure 2 This is a schematic diagram of the geometric structure of a linear sparse MIMO array cylindrical scanning SAR near-field imaging system according to an embodiment of the present application. Figure 2 As shown in Figure 1, a one-dimensional linear MIMO array can be mechanically scanned with a fixed radius R to form a cylindrical synthetic aperture. The radar transmitting array elements and receiving array elements are discretely distributed on a straight line perpendicular to the xoy plane, with position coordinates p and t=(Rcosθ,Rsinθ,z t ), p r =(Rcosθ,Rsinθ,z r ), where p t and p r are the coordinates of the transmitting and receiving elements in the synthetic aperture, respectively; R is the scanning radius; θ is the angle between the one-dimensional antenna array driven by the mechanical scanning device and the positive direction of the x-axis; z t With z r are the height coordinates of the transmitting array element and the receiving array element respectively. The scattering characteristics of the target are f(x, y, z), which is the objective function that the millimeter-wave near-field imaging system needs to reconstruct. x, y, and z represent the three-dimensional coordinate parameters in the space under the Cartesian coordinate system (x, y, z).

[0074] The transmitting antenna array elements sequentially radiate millimeter wave signals to illuminate the space where the target to be measured is located. The millimeter wave signal propagation process satisfies the first-order Born approximation. When propagation attenuation is ignored, the echo signal can be expressed as, but not limited to:

[0075]

[0076] Where s represents the echo signal; k represents the number of echoes, k = 2πf / c, c is the speed of light; j is the complex exponential term; f(·) represents the reflectivity distribution function of the target, which is also the objective function in the reconstruction process, R t and R r are the distances from the transmitting array element and the receiving array element to the target point in space, which can be expressed as follows:

[0077]

[0078] Among various image reconstruction methods, BPA is widely regarded as the gold standard due to its excellent imaging quality. Its formula can be expressed as:

[0079]

[0080] In formula (4), BPA reconstructs the imaging result pixel by pixel by performing a spatial wavenumber integration at each location within the imaging area. The computational effort is proportional to the number of sampling points in the imaging result, which is determined by the spectral bandwidth characteristics of the imaging result. For close-range SAR images, the spectral characteristics, including carrier frequency and bandwidth, have strong spatial variability.

[0081] Therefore, the embodiment of the present application can perform local spectrum analysis, and then simulate the spectrum characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system based on the local spectrum analysis results to analyze the local spectrum support domain of the image at any position (the area where the sub-image has a non-zero value in the spectrum space).

[0082] First, the spatial wavenumber domain representation of the linear sparse MIMO array cylindrical scanning SAR system image can be directly calculated by three-dimensional Fourier transform, that is:

[0083]

[0084] Where F(·) represents the image spectrum, k x 、k y 、k z represent the spatial wavenumber domain variables of x, y and z respectively.

[0085] By using the multi-dimensional stationary phase principle to approximately solve the triple integral of x, y, and z in formula (5), we can obtain:

[0086]

[0087] Where α and (x0, y0, z0) represent the slowly varying amplitude term and the stationary point, respectively. The phase term Φ in the SDC operation can be expressed as:

[0088]

[0089] The stationary point (x0, y0, z0) satisfies Right now:

[0090]

[0091] Substitute and solve, and we can get any k that satisfies the above equation = k0 = (k x ,k y ,k z ) can be expressed as:

[0092]

[0093] Where k=(k x ,k y ,k z ) represents the spatial wavenumber domain variable of the image spectrum, k x 、k y 、k z denote the spatial wavenumber domain counterparts of x, y, and z, respectively.

[0094] Formula (9) determines the relationship between the spatial wavenumber domain variables and system parameters of the linear MIMO array cylindrical scanning SAR imaging system. When the pixel point (x0, y0, z0) in the imaging interval is fixed, for any wavenumber domain variable k x 、k y 、k z , only when the system parameters k, θ, z t、z r When formula (9) is satisfied at the same time, the asymptotic integral result of formula (6) is non-zero, which is a set of a finite number of scattered points in the spatial wavenumber domain.

[0095] The above analysis is about the local spectrum concept of the linear MIMO array cylindrical scanning SAR imaging system. To simplify the subsequent analysis, the embodiment of the present application also expresses any k = k0 = (x0, y0, z0) determined by formula (9) as the wave number k, the transmitting and receiving antenna position p r 、p t And the function of any point p0=(x0,y0,z0) in space can be expressed as follows, but is not limited to:

[0096]

[0097] Here, p0 represents the stationary point in the stationary phase principle, and ||·|| represents the vector amplitude.

[0098] Thus, the spectrum support range of the SAR imaging result can be determined, which can be expressed as follows but is not limited to:

[0099]

[0100] Among them, SP A represents the spectrum support domain of the imaging result, D is the imaging area containing the imaging target, A represents the MIMO array aperture, k min 、k max Represent the minimum and maximum values ​​of the wave number respectively. For any position p0∈D in the imaging area, the corresponding spectrum support range can be calculated. The expression can be, but is not limited to, as follows:

[0101]

[0102] in, represents the spectral support region at any position p0 in the imaging area.

[0103] In the embodiment of the present application, it can be defined as the local spectrum support domain at position p0. According to the Casson bandwidth criterion, the spectrum support domain bandwidth of the global image is approximately equal to the union of the local spectrum support domains at each position in the imaging area, that is:

[0104]

[0105] Formulas (9)-(13) describe the support domain characteristics of the spectrum of a linear sparse MIMO array cylindrical scanning SAR image. Next, the embodiment of the present application can derive the Nyquist sampling rate of each dimension accordingly, and the expression can be, but is not limited to, expressed as:

[0106]

[0107] Among them, k x min , k x max , k y min , k y max , k z min and k z max represent k determined by formula (9) x , k y and k z The lower and upper limits of .

[0108] From this, we can see that the resulting image from a linear MIMO array cylindrical scanning SAR system can be modeled as a spatially varying, band-limited 3D signal, and the spectral support domain of the image at any position can be analyzed to obtain it. Based on this, embodiments of the present application can design an efficient spectrum compression method combined with decomposition technology based on the spectral support domain of the image at any position, thereby reducing sampling requirements and thus reducing the computational load of the time domain image reconstruction method.

[0109] Specifically, the embodiments of the present application can analyze the approximate minimum volume bounding box of the local spectrum support domain to convert the actual bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain of the target linear sparse MIMO array cylindrical scanning SAR system, and obtain the restricted support range of the local spectrum. The restricted support range can effectively narrow the aperture range, thereby reducing the sampling amount and further reducing the computational complexity.

[0110] First, the embodiment of the present application can analyze and derive an approximate minimum volume bounding box (a geometric shape used to enclose the local spectral support domain) of the local spectral support domain based on the spectral support domain of the image at any position.

[0111] Figure 3 This is a schematic diagram of the convex hull, key points, and minimum volume bounding box of the sub-image local spectrum support domain under different linear sparse MIMO arrays in one embodiment of the present application. Figure 3 As shown in FIG, the outline of the local spectrum support domain is an irregular three-dimensional body with a smooth surface, a depression at the bottom and a bulge in the middle, and a large scanning angle dimension span.

[0112] In order to maximize the spectrum occupancy of the sub-image after the local spectrum transformation, the embodiment of the present application can use, but is not limited to, key points in the spatial wavenumber domain to represent the approximate minimum volume bounding box of the local spectrum at any position p in the sub-array SA and the imaging area. The definition expression can be expressed as follows:

[0113]

[0114] Among them, θ ζ,SAIt represents the sub-array scanning angle corresponding to the minimum difference in distance between the target point and the projection point of each antenna on the xoy plane. The calculation method is: for the circle radius R where the antenna is located, the sub-array scanning angle is θ scan,SA , let each scanning angle θ i ∈θ scan,SA The corresponding antenna coordinate projection on the xoy plane is (Rcosθ i ,Rsinθ i ), and calculate the Euclidean distance d of the projected point domain target position (x0, y0) i :

[0115]

[0116] If all distances are consistent, it is considered that the distances from the target to all scanning angles of the subarray are almost the same. In this case, θ ζ,SA Take the middle value of the scanning angle, otherwise, select the angle that minimizes the target distance from all scanning angles as θ ζ,SA , defined as follows:

[0117]

[0118] In addition, t,min,SA 、z t,max,SA 、z r,min,SA 、z r,max,SA k represents the minimum and maximum z-coordinates of the receiving subarray and the minimum and maximum z-coordinates of the transmitting subarray, respectively. max Defined as the maximum wave number, θ min,SA and θ max,SA Indicates the minimum scanning angle and maximum scanning angle of the subarray. t,ζ,SA With z r,ζ,SA They represent the transmitting and receiving antennas closest to z0 in the transmitting and receiving subarrays, respectively, and can be defined as follows:

[0119]

[0120] The key points defined by formula (15) characterize the orientation information of the local spectrum support domain, where k1 and k2 are used to describe the boundary points of the support domain in the z direction, while k3 and k4 correspond to the boundary points in the y direction.

[0121] Furthermore, embodiments of the present application also require further determination of the boundary points of the distance dimension. Considering that the closest and farthest points of the support domain in the distance direction are different for the relative positions of different MIMO linear array transceiver elements, embodiments of the present application can be analyzed in the following three cases, but are not limited to them.

[0122] 1. In the case of separate transmit and receive antennas, the farthest scatter point in the support domain is determined by the transmit and receive antennas with the shortest distance, while the closest scatter point is contributed by the transmit and receive antennas with the longest distance. When the scan angle is increased in the azimuth direction, the closest scatter point in the support domain is also affected by the boundary points of the scan angle. Therefore, the key points in the range direction of the support domain in the corresponding three-dimensional wavenumber domain can be expressed as, but not limited to:

[0123]

[0124] Among them, θ mid,SA is the midpoint of the subarray scanning angle, k min is the minimum value of the wave number. Moreover, formula (20) is for the case where the coordinates of the transmitting array elements are all smaller than the coordinates of the receiving array elements. When the positions of the transmitting array elements are all larger than the coordinates of the receiving array elements, the z values ​​of k5 and k6 are t,min,SA With z t,max,SA 、z r,max,SA With z r,min,SA They should exchange positions.

[0125] 2. In the case of intersecting distribution of transmitting and receiving antennas, the selection of the closest point in the support domain distance direction is closely related to the position of the target point. Specifically, when the target is z t,min,SA 、z r,max,SA The sum of the distances between it and the other two ends z t,max,SA 、z r,min,SA When the sum of the distances is taken, the nearest point of the support domain is considered to be at z t,min,SA 、z r,max,SA The transmitting and receiving antennas at the position contribute together; otherwise, the closest point is z t,max,SA 、z r,min,SA In addition, considering the influence of the scanning angle, the average of the maximum and minimum scanning angles of the subarray is taken, so k5 can be determined by, but is not limited to, the following formula:

[0126]

[0127] Among them, γ1 represents the transmit and receive antenna array element (R,θ min,SA ,z t,min,SA )、(R,θ min,SA ,z r,max,SA ) and (R,θ max,SA ,z t,min,SA )、(R,θ max,SA ,z r,max,SA ) to the spatial point (x0, y0, z0), γ2 represents the Euclidean distance of the transmitting and receiving antenna elements (R, θ min,SA ,z t,max,SA )、(R,θ min,SA ,z r,min,SA) and (R,θ max,SA ,z t,max,SA )、(R,θ max,SA ,z r,min,SA ) are the Euclidean distances to the spatial points (x0, y0, z0) respectively.

[0128] When calculating the farthest point in the support domain, it is considered that when the imaging range is large enough to include all near-field areas, the wavenumber domain bandwidth of the near-field MIMO radar image can reach up to 2k in the range direction. max , but if we directly regard the key point k6 farthest from the point as having a modulus of 2k max , the direction is k5 / |k5|, then the minimum volume bounding box of the spectrum support domain will contain a large area of ​​blank area, which will eventually reduce the efficiency of spectrum compression. At the same time, it will cause the k6 vector to be a rotation field, and subsequent processing requires additional derotation field transformation, which increases the complexity of the calculation.

[0129] Therefore, in the embodiment of the present application, an approximate process is used to calculate the farthest point in the support region: let the distance from the target point to the two end points z of the transmitting antenna array be t,min,SA 、z t,max,SA The angle formed by the two lines is α, and its angle bisector λ is obtained. t The position of the transmitting antenna closest to the intersection of the straight line where the transmitting array is located is the corresponding z t,ξ,SA .

[0130] Similarly, let the target point to the two end points z of the receiving antenna array r,min,SA 、z r,max,SA The angle formed by the two lines is β, and its angle bisector λ is obtained. t The receiving antenna position closest to the intersection of the straight line where the receiving array is located is the corresponding z r,ξ,SA .

[0131] By this method, the embodiment of the present application can accurately and effectively approximate the maximum distance of the support region, while also avoiding the introduction of excessive blank areas and additional derotation field calculations within the minimum volume bounding box. At this time, k6 can be determined by, but is not limited to, the following formula:

[0132] k6=((R,θ mid,SA ,z t,ξ,SA ),(R,θ mid,SA ,z r,ξ,SA ),(x0,y0,z0),k max ) (twenty two)

[0133] 3. In the case of inclusive distribution of transmitting and receiving antennas, the selection strategy of the farthest point and the nearest point is basically the same as that of the intersection distribution. However, due to the change in the antenna arrangement, when the target point is at a different position, the corresponding "nearest antenna pair" and "farthest antenna pair" will also change accordingly. Therefore, the key points k5 and k6 in the distance direction are still determined by formulas (21) and (22).

[0134] From the perspective of geometry, the local spectrum support domain in the above three cases is in the azimuth direction k. y With k z Usually shows a fan-shaped or curved appearance, x The corresponding arc transition is affected by factors such as antenna distribution and target position. Figure 3 It can also be observed that the parallelepiped formed by vectors k2-k1, k4-k3, and k6-k5 can be approximated as the minimum volume bounding box of the local spectrum. Based on this, embodiments of the present application can use SDC and LLT to convert the local spectrum bounding box into a unit box located at the origin of the spatial wavenumber domain to achieve a spectral compression effect.

[0135] Here, SDC is implemented by multiplying the sub-image in the spatial domain with a spatially variable phase term, assuming f m,n is the imaging result of any sub-array, then the SDC operation formula can be expressed as but not limited to:

[0136] f m,n '(p)=f m,n (p)e -jΦ (twenty three)

[0137] Among them, f m,n '(p) represents the reconstruction result of the m-th level sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th level sub-image, Φ is also a function of different positions (x, y, z) in space and the frequency of the transmitted signal, and its gradient should be the center k of the local spectrum support domain c , that is, satisfying:

[0138]

[0139] Here k c The key points in the spatial wavenumber domain can be expressed as, but not limited to:

[0140]

[0141] According to different MIMO array arrangements, when the transmitting array element and the receiving array element of the MIMO array are completely separated in space, its k c The scalar potential function Φ can be expressed as, but not limited to:

[0142]

[0143] Among them, A1, A2 and B1, B2, B3, B4 can be defined as follows:

[0144]

[0145] It should be noted that formula (27) only gives the case where the coordinates of all transmitting array elements are less than or equal to the coordinates of the receiving array elements. When the position of the transmitting array element is greater than the coordinates of the receiving array element, the z in A1 and A2 is t,max,SA 、z r,min,SA Should be replaced by z t,min,SA 、z r,max,SA , z of B1, B2, B3, B4 t,min,SA With z t,max,SA 、z r,max,SA With z r,min,SA They should exchange positions.

[0146] When the transmitting elements and receiving elements of the MIMO array intersect or form an inclusion relationship, if γ1≤γ2, then A1 and A2 in formula (27) should be updated to the following expressions:

[0147]

[0148] If γ1>γ2, it is still necessary to update A1 and A2 in formula (27) to formula (28), and at the same time, the z values ​​of B1, B2, B3, and B4 are t,min,SA With z t,max,SA 、z r,max,SA With z r,min,SA They should exchange positions.

[0149] Next, the embodiment of the present application may perform a down-conversion operation on the local spectrum. Figure 4 This is a schematic diagram of the support domain of the sub-image spectrum after SDC and LLT transformation in one embodiment of the present application. Figure 4 As shown in (a) and (b), after SDC, the local spectrum support domain will be moved to the origin of the spatial wavenumber domain, thereby significantly reducing the sampling rate required for the sub-image.

[0150] Subsequently, the embodiment of the present application can use LLT to convert the bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain to further improve the spectrum occupancy. The formulas of the three generating vectors v1, v2, and v3 of the parallelogram of the local spectrum support domain can be, but are not limited to, expressed as:

[0151]

[0152] Merge the linear transformations (including scaling, rotation, and shearing transformations) in the spatial wavenumber domain for the local spectral support domain into the linear coordinate transformation matrix T k In the spatial wavenumber domain, the LLT operation can be expressed as:

[0153] 2πE=T k [v1 v2 v3] (30)

[0154] Where E is the identity matrix, T k is the linear coordinate transformation matrix.

[0155] At this point, after SDC and LLT, the spectrum of the sub-image is confined to a cubic box centered at the origin with a side length of 2π, which corresponds to a unit sampling rate in each dimension. Therefore, T k The solution formula can be expressed as but not limited to:

[0156] T k =2π[v1 v2 v3] -1 (31)

[0157] According to the properties of Fourier transform, the required local linear coordinate transformation T in the spatial domain can be derived s It can be expressed as, but not limited to:

[0158]

[0159] Furthermore, in order to implement LLT for sub-images in the spatial domain, the embodiment of the present application calculates a uniform sampling grid with a uniform sampling rate for each sub-array in the UVN coordinate system (u, v, n), so it is necessary to establish a coordinate transformation And locally linearize it into the required local linear transformation. The formula can be, but is not limited to, expressed as follows:

[0160]

[0161] According to formula (33), u(x,y,z), v(x,y,z), and n(x,y,z) are the scalar potential functions of the vector fields v1 / 2π, v2 / 2π, and v3 / 2π, respectively. u(x,y,z) and v(x,y,z) can be solved as follows, but are not limited to:

[0162]

[0163] Due to different MIMO array arrangements, the farthest and closest points in the spectrum support domain are defined differently, resulting in different vector fields v3, which will lead to different solutions for n(x, y, z).

[0164] When the transmitting elements and receiving elements of a MIMO array are completely separated in space, n(x,y,z) can be expressed as, but not limited to:

[0165]

[0166] Similarly, formula (36) only gives the case where the coordinates of all transmitting array elements are less than or equal to the coordinates of the receiving array elements. When the position of the transmitting array element is greater than the coordinates of the receiving array element, the z of A1 and A2 in n(x, y, z) is t,max,SA 、z r,min,SA The variables should be replaced by z t,min,SA 、z r,max,SA Variable, variable z in B1, B2, B3, and B4 t,min,SA With z t,max,SA 、z r,max,SA With z r,min,SA They should exchange positions.

[0167] When the transmitting elements and receiving elements of the MIMO array intersect or form an inclusion relationship, if γ1≤γ2, A1 and A2 in formula (36) should be updated to the values ​​of formula (28); if γ1>γ2, A1 and A2 in formula (36) also need to be updated to formula (28), and the z values ​​of B1, B2, B3, and B4 are t,min,SA With z t,max,SA 、z r,max,SA With z r,min,SA So far, the required local linear coordinate transformation relationship in the spatial domain is determined by formula (34) to formula (36).

[0168] The support range of the local spectrum after SDC and LLT is the limited support range of the local spectrum in the embodiment of the present application as shown in the attached Figure 4 (c) shows the spectrum distribution within the unit box at the origin in the spatial wavenumber domain.

[0169] After the above processing, in actual application, the embodiment of the present application can perform SDC down-conversion operation on the local spectrum of the target linear sparse MIMO array cylindrical scanning SAR system through formula (23), and then based on the coordinate transformation LLT is used to calculate a uniform sampling grid with a uniform sampling rate for each subarray in the (u, v, n) coordinate system, so that sampling can be performed on the uniform sampling grid and the subimage of each subarray can be reconstructed with the least number of sampling points.

[0170] Then, the embodiment of the present application can convert the uniform sampling network into a non-uniform sampling grid in the (x, y, z) coordinate system, so as to reconstruct the sub-image corresponding to each sub-array in the non-uniform grid corresponding to each sub-array.

[0171] The embodiments of the present application can analyze the shape of the wavenumber domain spectrum at different locations in space, analytically represent the space-varying characteristics (spectral characteristics) of the SAR image spectrum, and derive analytical expressions for SDC and LLT based on these spectral characteristics. These expressions are then used to perform spectral compression of sub-images, thereby improving spectrum occupancy and reducing the number of sampling points. This effectively solves the problem of complex numerical pre-calculation required for spatial down-conversion (SDC) and local linear transform (LLT) operations during the spectrum compression process.

[0172] Step S103 : superimposing the sub-images of each sub-array according to a preset superposition rule to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system.

[0173] In certain embodiments, after reconstructing the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each sub-array, the present application can superimpose the sub-images of each sub-array according to a preset superposition rule to obtain the final three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system.

[0174] Among them, the preset superposition rule here can be understood as the pre-set superposition rule when multiple sub-images are superimposed to form the final three-dimensional imaging result. For example, all sub-images are divided into multiple groups according to a set number, and then the multiple groups are superimposed until the final SAR three-dimensional imaging result is obtained.

[0175] Optionally, in one embodiment of the present application, the sub-images of each sub-array are superimposed according to a preset superposition rule to obtain three-dimensional imaging of the target linear sparse MIMO array cylindrical scanning SAR system, including: performing space-variant spatial down-conversion processing on the sub-images of each sub-array to obtain compressed sub-images; interpolating the compressed sub-images onto the adjacent higher-level sampling grid of the corresponding level of each sub-array to perform pairwise coherent superposition on the sub-images of each sub-array to obtain multiple sub-image pairs, and repeating the pairwise coherent superposition until a unique sub-image pair is obtained; and determining the three-dimensional imaging result based on the unique sub-image pair. The expression for the space-variant spatial down-conversion processing is:

[0176] f m,n '(p)=f m,n (p)e -jΦ

[0177]

[0178] Among them, f m,n '(p) represents the reconstruction result of the m-th level sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th level sub-image, j represents the complex exponential term; Φ is a function of different positions (x, y, z) in space and the frequency of the transmitted signal, kmax 、k min They represent the maximum and minimum values ​​of the wave number respectively. A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process.

[0179] It can be understood based on the relevant descriptions of other embodiments that the present application can superimpose the sub-images of each sub-array according to a certain superposition rule to obtain a final three-dimensional image.

[0180] For example, in order to minimize the interference caused by superposition, the present application can, but is not limited to, using pairwise coherent superposition of sub-images as a certain superposition rule. Based on this superposition rule, the present application can pairwise coherent superposition of the sub-images of each sub-array to obtain sub-image pairs formed after the sub-images are superimposed.

[0181] Then, in the embodiment of the present application, the sub-image pair can be used as a new sub-image, and the sub-image pair can be coherently superimposed in pairs to obtain a new sub-image pair.

[0182] Then, the new sub-image pair is used as a new sub-image for pairwise coherent superposition, and the pairwise coherent superposition process is repeated until a unique sub-image pair is obtained. The unique sub-image is the final SAR three-dimensional imaging result.

[0183] It should be noted that the specific superposition rules can be determined or adjusted by professionals skilled in the art based on actual conditions or needs. The embodiments of this application are merely illustrative and not limiting. For example, three sub-images may be used as the basis for superposition, i.e., coherent superposition of three sub-images may be performed each time.

[0184] In certain embodiments, the process of superimposing sub-images according to certain superposition rules includes an interpolation process, i.e., the sub-images of each sub-array are interpolated onto a sampling grid at an adjacent higher level (the level of each sub-array before superposition is used as the level reference, and the level to which the sub-images after superposition belong is called the adjacent higher level) of the level at which each sub-array is located, thereby achieving pairwise coherent superposition of the sub-images of each sub-array on the sampling grid at the adjacent higher level.

[0185] To avoid aliasing during the interpolation process, the embodiment of the present application may first perform space-variant spatial down-conversion processing on the sub-image of each sub-array to convert the sub-image of each sub-array into a compressed sub-image. The expression for the space-variant spatial down-conversion processing is the same as that of formulas (23) and (26):

[0186] f m,n '(p)=f m,n (p)e -jΦ (37)

[0187]

[0188] Among them, f m,n '(p) represents the reconstruction result of the m-th level sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th level sub-image, j represents the complex exponential term; Φ is a function of different positions (x, y, z) in space and the frequency of the transmitted signal, k max 、k min They represent the maximum and minimum values ​​of the wave number respectively. A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process.

[0189] After converting the sub-image pair into a compressed sub-image, the embodiment of the present application can interpolate the compressed sub-image to a sampling grid one level higher than the sub-array level before the sub-image pair was superimposed, and then restore the carrier of the compressed new sub-image by multiplying the sub-image with the conjugate space-variant spatial down-conversion function, ensuring the coherent accumulation of the sub-image to obtain the processed sub-image.

[0190] Finally, the sub-images processed by pairwise coherent superposition can be obtained on adjacent high-level sampling grids to obtain sub-image pairs, and the sub-image pairs can be used as new sub-images. The new sub-image pairs are then subjected to the same transformation processing, interpolation processing, and pairwise coherent superposition processing until the final three-dimensional imaging result is obtained.

[0191] Additionally, under the superposition rule of pairwise coherent superposition, when dividing a complete integrated array into multiple sub-arrays, professionals in this technical field may, but are not limited to, first determine a decomposition level and then divide the complete integrated array into multiple sub-arrays based on the decomposition level, because the decomposition level is related to the operation speed and interpolation error.

[0192] Here, the decomposition level can be understood as the level (L levels) between the level corresponding to the multiple decomposed subarrays (the first level, the lowest level) and the level corresponding to the final 3D imaging result (the Lth level, the highest level). For example, if the decomposition level is 4, then the first-level subarrays are 8, the second-level subarrays are 4, the third-level subarrays are 2, and the fourth-level subarrays are 1, which is the final complete integrated array, that is, the complete synthetic aperture.

[0193] If you want to improve the calculation accuracy, you can appropriately lower the decomposition level, that is, reduce the number of sub-arrays divided into the complete integrated array, thereby reducing the number of interpolations and reducing the interpolation error; if you want to increase the calculation speed, you can appropriately increase the decomposition level, that is, increase the number of sub-arrays divided into the complete integrated array.

[0194] It should be noted that the specific decomposition level can be set or adjusted by professional and technical personnel in this technical field according to actual conditions and actual needs. The embodiments of this application are only for illustrative purposes and are not specifically limited.

[0195] The present application is explained in detail below using a specific embodiment.

[0196] Figure 5 This is a flow chart of a three-dimensional fast near-field imaging method for a linear sparse MIMO array cylindrical scanning SAR system according to an embodiment of the present application. Figure 5 As shown, the method includes the following steps:

[0197] Step 1: The embodiment of the present application first obtains the original echo data set, and then corrects the amplitude and delay inconsistencies between different channel signals and the amplitude imbalance between different frequencies of each channel signal to obtain the complete echo data s(k, θ, z t ,z r );

[0198] Step 2: For a decomposition level of L, the complete integrated array (integrated aperture) can be divided into 2 L-1 sub-arrays and extract the corresponding sub-array SA from the complete echo data 1,n echo data;

[0199] Step 3: Use LLT to calculate the uniform sampling grid with uniform sampling rate in the UVN coordinate system (u, v, n) for each level 1 subarray (the lowest level subarray) and convert it into a non-uniform sampling grid G ​​in the (x, y, z) coordinate system. 1,n ; Where (u, v, n) is the coordinate after LLT transformation;

[0200] Step 4: Based on the non-uniform sampling grid, use the BPA method to reconstruct the sub-image of each level 1 sub-array on the sampling grid corresponding to each sub-array, that is, the first-level sub-image f 1,n At this time, due to the reduction of aperture, the resolution of the sub-image is reduced, which will cause the sub-image to be 1,n The sampling rate is reduced, thus reducing the overall computational burden;

[0201] Step 5: First Level 2 L-1 Sub-images form 2 L-2 sub-image pairs, including sub-image f 1,2n-1 and f 1,2n , coherent superposition to obtain the second-level sub-image f 2,n ;

[0202] Similarly, the imaging result of the mth level is obtained by coherently superimposing the sub-images of the m-1th level. This involves an interpolation process. To avoid aliasing during the interpolation process, the m-1th level sub-image is first subjected to a spatial down-conversion operation to convert the sub-image into a compressed form f' m-1,2n-1 and f' m-1,2n ;

[0203] Interpolate these m-1th level sub-images with well-defined spectral support ranges to the m-level sampling grid G m,n Then, the carrier is recovered by multiplying the sub-image with the conjugate space-variant spatial down-conversion function to ensure the coherent accumulation of the sub-image:

[0204] f m,n (p) = f' m,n (p)e jΦ (39)

[0205] Then, the processed m-1th level sub-images are coherently superimposed using the formula to obtain G m,n Upsampled m-th level sub-image:

[0206] f m,n (p) = f m-1,2n-1 (p)+f m-1,2n (p),p∈G m,n (40)

[0207] Where p represents the coordinates of the scattering point in the imaging area, G m,n Represents a non-uniform sampling grid at level m in (x,y,z) coordinates.

[0208] This operation is repeated to finally obtain the imaging result of the last level, which is the three-dimensional imaging result of the linear sparse MIMO cylindrical scanning SAR system.

[0209] Figure 6 This is a schematic diagram comparing the three-dimensional imaging results of an embodiment of the present application. Figure 6 As shown, the computational complexity of the BPA method used as a benchmark is O(N 5 ) level, the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system in the embodiment of the present application can achieve almost the same imaging quality as the benchmark back-projection method (BPA), but only requires O(N 3 log N) computational complexity, achieving accurate and efficient near-field 3D imaging of an arbitrary sparse linear MIMO array cylindrical scanning SAR system.

[0210] According to the near-field imaging method of a sparse MIMO array cylindrical scanning SAR system proposed in an embodiment of the present application, a complete integrated array can be divided into multiple subarrays, a downsampled subimage of each subarray is reconstructed, and then the subimages are superimposed to obtain the near-field three-dimensional imaging of the target linear sparse MIMO array cylindrical scanning SAR system. Thus, by analyzing the local spectral characteristics, the analytical expressions of SDC and LLT are innovatively derived, which are used to perform spectral compression of the subimages, thereby reducing the number of sampling points and overcoming the problems of high time complexity and computational complexity of traditional time-domain imaging methods. In implementation, the time-domain imaging method is used to reconstruct the downsampled subimage of each subarray, and the subimages of subsequent levels are obtained by pairwise merging, ultimately obtaining an accurate three-dimensional reconstruction result, thus achieving accurate and efficient three-dimensional imaging of any linear sparse MIMO array cylindrical scanning SAR system. Thus, the conventional imaging method and the range migration method based on non-uniform fast Fourier transform in the related art are solved, which still need to be improved in imaging speed, and with the increase of array sparsity, the imaging quality and computational efficiency also have certain limitations.

[0211] Next, a near-field imaging device of a sparse MIMO array cylindrical scanning SAR system proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0212] Figure 7 Schematic diagram of the structure of the near-field imaging device of the sparse MIMO array cylindrical scanning SAR system in an embodiment of the present application.

[0213] like Figure 7 As shown, the near-field imaging device 10 of the sparse MIMO array cylindrical scanning SAR system includes: an acquisition module 100 , a reconstruction module 200 and an imaging module 300 .

[0214] Among them, the acquisition module 100 is used to obtain complete echo data of a complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system, and divide the complete integrated array into multiple sub-arrays to select local echo data corresponding to multiple sub-arrays from the complete echo data.

[0215] The reconstruction module 200 is configured to reconstruct a sub-image corresponding to each sub-array in a non-uniform sampling grid corresponding to each sub-array in a plurality of sub-arrays based on the local echo data.

[0216] The imaging module 300 is used to superimpose the sub-images of each sub-array according to a preset superposition rule to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system.

[0217] Optionally, in one embodiment of the present application, the acquisition module 100 includes: an acquisition unit for acquiring the original echo data of the complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system; a correction unit for correcting the original echo data based on the amplitude inconsistency, delay inconsistency and amplitude imbalance between multiple channel signals at different frequencies in the target linear sparse MIMO array cylindrical scanning SAR system to obtain complete echo data.

[0218] Optionally, in one embodiment of the present application, it further includes: a simulation module, an analysis module and a calculation module.

[0219] The simulation module is used to simulate the spectral characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system based on the local spectrum analysis results of the target linear sparse MIMO array cylindrical scanning SAR system before reconstructing the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each sub-array in the multiple sub-arrays, so as to analyze the local spectrum support domain of the image at any position.

[0220] An analysis module is used to analyze the approximate minimum volume bounding box of the local spectrum support domain to convert the actual bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain of the target linear sparse MIMO array cylindrical scanning SAR system, thereby obtaining the restricted support range of the local spectrum.

[0221] The calculation module is used to calculate a uniform sampling grid with a uniform sampling rate for each subarray in a (u, v, n) coordinate system based on a restricted support range, so as to convert the uniform sampling grid into a non-uniform sampling grid in a (x, y, z) coordinate system.

[0222] Alternatively, in one embodiment of the present application, a calculation formula for a uniform sampling grid with a uniform sampling rate for each subarray in the (u, v, n) coordinate system can be expressed as:

[0223]

[0224]

[0225] Among them, u(x,y,z), v(x,y,z), and n(x,y,z) represent the transformed (u,v,n) coordinates respectively; k max 、k min Represent the maximum and minimum values ​​of the wave number respectively; x0, y0, z0 are the parameters in (x0, y0, z0), (x0, y0, z0) represents the slowly changing stationary point; R represents the scanning radius of the system; z t,ζ,SA With z r,ζ,SARespectively represent the transmitting and receiving antennas closest to z0 in the transmitting and receiving subarrays, z t,min,SA 、z t,max,SA 、z r,min,SA 、z r,max,SA Respectively represent the minimum and maximum values ​​of the z coordinate of the receiving subarray and the minimum and maximum values ​​of the z coordinate of the transmitting subarray; A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process; θ ζ,SA The subarray scanning angle corresponding to the minimum distance difference between the target point and the projection point of each antenna on the xoy plane is θ; min,SA and θ max,SA represents the minimum scanning angle and maximum scanning angle of the subarray, θ mid,SA is the midpoint of the subarray scan angle.

[0226] Optionally, in one embodiment of the present application, the imaging module 300 includes: a processing unit, a superposition unit, and a determination unit.

[0227] The processing unit is used to perform spatial down-conversion processing on the sub-image of each sub-array to obtain a compressed sub-image.

[0228] The superposition unit is used to interpolate the compressed sub-images onto the adjacent higher-level sampling grid of the corresponding level of each sub-array, so as to perform pairwise coherent superposition on the sub-images of each sub-array to obtain multiple sub-image pairs, and repeat the pairwise coherent superposition until a unique sub-image pair is obtained.

[0229] The determining unit is configured to determine a three-dimensional imaging result based on a unique sub-image pair.

[0230] Optionally, in one embodiment of the present application, the expression for the space-variable spatial down-conversion processing is:

[0231] f m,n '(p)=f m,n (p)e -jΦ

[0232]

[0233] Among them, f m,n '(p) represents the reconstruction result of the m-th level sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th level sub-image, j represents the complex exponential term; Φ is a function of different positions (x, y, z) in space and the frequency of the transmitted signal, k max 、k min They represent the maximum and minimum values ​​of the wave number respectively. A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process.

[0234] It should be noted that the above explanation of the embodiment of the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system is also applicable to the near-field imaging device of the sparse MIMO array cylindrical scanning SAR system of this embodiment, and will not be repeated here.

[0235] According to the near-field imaging device of the sparse MIMO array cylindrical scanning SAR system proposed in the embodiment of the present application, the complete integrated array can be divided into multiple subarrays, and the downsampled subimages of each subarray are reconstructed. Then, the near-field three-dimensional imaging of the target linear sparse MIMO array cylindrical scanning SAR system is obtained by superimposing the subimages. Thus, by analyzing the local spectral characteristics, the analytical expressions of SDC and LLT are innovatively derived, and the spectral compression of the subimages is used to reduce the number of sampling points, thereby overcoming the problems of high time complexity and computational complexity of traditional time domain imaging methods. Then, the time domain imaging method is used to reconstruct the downsampled subimages of each subarray, and the subimages of subsequent levels are obtained by pairwise merging, finally obtaining an accurate three-dimensional reconstruction result, realizing accurate and efficient three-dimensional imaging of any linear sparse MIMO array cylindrical scanning SAR system. Thus, the conventional imaging method and the range migration method based on non-uniform fast Fourier transform in the related art are solved. The imaging speed still needs to be improved, and the imaging quality and computational efficiency also have certain limitations as the array sparsity increases.

[0236] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0237] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0238] When the processor 802 executes the program, the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system provided in the above embodiment is implemented.

[0239] Furthermore, the electronic device further includes:

[0240] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0241] The memory 801 is used to store computer programs that can be run on the processor 802.

[0242] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0243] If the memory 801, processor 802, and communication interface 803 are implemented independently, the communication interface 803, memory 801, and processor 802 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0244] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0245] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0246] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned near-field imaging method for a sparse MIMO array cylindrical scanning SAR system.

[0247] An embodiment of the present application also provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system provided in the embodiment of the present application is implemented.

[0248] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0249] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0250] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0251] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0252] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0253] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0254] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0255] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A near-field imaging method for a sparse MIMO array cylindrical scanning SAR system, characterized in that: The following steps are involved: Acquire complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system, and divide the complete integrated array into a plurality of subarrays to select local echo data corresponding to the plurality of subarrays from the complete echo data; reconstructing a sub-image corresponding to each sub-array in a non-uniform sampling grid corresponding to each sub-array in the plurality of sub-arrays based on the local echo data; The sub-images of each sub-array are superimposed according to a preset superposition rule to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system.

2. The near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to claim 1, characterized in that: The method of obtaining complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system includes: Acquiring raw echo data of a complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system; Based on the amplitude inconsistency, delay inconsistency and amplitude imbalance between the multiple channel signals in the target linear sparse MIMO array cylindrical scanning SAR system at different frequencies, the original echo data is corrected to obtain the complete echo data.

3. The near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to claim 1, characterized in that: Before reconstructing the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each sub-array in the plurality of sub-arrays, the method further includes: Based on the local spectrum analysis results of the target linear sparse MIMO array cylindrical scanning SAR system, simulating the spectrum characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system to analyze the local spectrum support domain of the image at any position; Analyzing the approximate minimum volume bounding box of the local spectrum support domain to convert the actual bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain of the target linear sparse MIMO array cylindrical scanning SAR system to obtain a restricted support range of the local spectrum; Based on the restricted support range, a uniform sampling grid with a uniform sampling rate for each subarray in a (u, v, n) coordinate system is calculated to convert the uniform sampling grid into a non-uniform sampling grid in a (x, y, z) coordinate system.

4. The near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to claim 3, characterized in that: The calculation formula for the uniform sampling grid with uniform sampling rate for each sub-array in the (u, v, n) coordinate system is: Among them, u(x,y,z), v(x,y,z), and n(x,y,z) represent the transformed (u,v,n) coordinates respectively; k max 、k min Represent the maximum and minimum values ​​of the wave number respectively; x0, y0, z0 are the parameters in (x0, y0, z0), (x0, y0, z0) represents the slowly changing stationary point; R represents the scanning radius of the system; z t,ζ,SA With z r,ζ,SA Respectively represent the transmitting and receiving antennas closest to z0 in the transmitting and receiving subarrays, z t,min,SA 、z t,max,SA 、z r,min,SA 、z r,max,SA Respectively represent the minimum and maximum values ​​of the z coordinate of the receiving subarray and the minimum and maximum values ​​of the z coordinate of the transmitting subarray; A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process; θ ζ,SA The subarray scanning angle corresponding to the minimum distance difference between the target point and the projection point of each antenna on the xoy plane is θ; min,SA and θ max,SA represents the minimum scanning angle and maximum scanning angle of the subarray, θ mid,SA is the midpoint of the subarray scan angle.

5. The near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to claim 1, characterized in that: The superimposing of the sub-images of each sub-array according to a preset superposition rule to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system includes: Performing space-variant down-conversion processing on the sub-image of each sub-array to obtain a compressed sub-image; interpolating the compressed sub-images onto a sampling grid at an adjacent higher level corresponding to the level of each sub-array, so as to perform pairwise coherent superposition on the sub-images of each sub-array to obtain a plurality of sub-image pairs, and repeating the pairwise coherent superposition until a unique sub-image pair is obtained; The three-dimensional imaging result is determined based on the unique sub-image pair.

6. The near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to claim 5, characterized in that: The expression of the space-variable down-conversion process is: f m,n '(p)=f m,n (p)e -jΦ Among them, f m,n '(p) represents the reconstruction result of the m-th level sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th level sub-image, j represents the complex exponential term; Φ is a function of different positions (x, y, z) in space and the frequency of the transmitted signal, k max 、k min They represent the maximum and minimum values ​​of the wave number respectively. A1, A2 and B1, B2, B3, B4 are transition parameters designed to facilitate the calculation process.

7. A near-field imaging device for a sparse MIMO array cylindrical scanning SAR system, characterized in that: include: an acquisition module, configured to acquire complete echo data of a complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system, and divide the complete integrated array into a plurality of subarrays, so as to select local echo data corresponding to the plurality of subarrays from the complete echo data; a reconstruction module, configured to reconstruct a sub-image corresponding to each sub-array in a non-uniform sampling grid corresponding to each sub-array in the plurality of sub-arrays based on the local echo data; An imaging module is used to superimpose the sub-images of each sub-array according to a preset superposition rule to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed, it is used to implement the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system according to any one of claims 1 to 6.

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