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

Abstract

The application relates to the technical field of millimeter wave near-field imaging, in particular to a near-field imaging method of a sparse MIMO array cylindrical scanning SAR system, wherein the method comprises the following steps: dividing a complete comprehensive array of the SAR system into multiple sub-arrays, selecting local echo data corresponding to the multiple sub-arrays from complete echo data, calculating a uniform sampling grid with a unified sampling rate of each primary sub-array in a (u, v, n) coordinate system, and converting the uniform sampling grid into a primary non-uniform sampling grid in an (x, y, z) coordinate system, reconstructing a primary sub-image in the primary non-uniform sampling grid based on the local echo data, and iteratively performing a two-by-two coherent superposition process to obtain a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system. The application can effectively reduce the calculation complexity of imaging, and realizes accurate and efficient near-field three-dimensional imaging of an arbitrary linear sparse MIMO array cylindrical scanning SAR system.

Description

Technical Field

[0001] This 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 Technology

[0002] Near-field active array-based millimeter-wave imaging technology can achieve high spatial resolution while maintaining large apertures and wide operating bandwidths. Combined with the non-ionizing and penetrating properties of millimeter waves, this technology has been widely studied and applied in various fields, including security inspection, medical diagnostics, through-wall imaging, and non-destructive testing.

[0003] In applications such as security inspection, 3D millimeter-wave imaging systems not only require high performance but also omnidirectional illumination of the target, minimizing blind spots and enabling image acquisition over a wider field of view. Cylindrical scanning SAR systems, due to their multi-angle observation advantages, have become a highly competitive solution and have attracted widespread research attention. Compared to traditional planar scanning SAR systems, cylindrical scanning SAR systems use a fixed-radius rotating scan to form a cylindrical synthetic aperture, achieving omnidirectional target illumination. Furthermore, the sparse MIMO array further reduces the number of physical elements and actual channels, resulting in shorter data acquisition time. The array's sparsity effectively reduces sidelobe interference, providing richer spectral information and a higher dynamic range, thus improving image quality.

[0004] However, in related technologies, there is still a lack of accurate and efficient 3D imaging methods for cylindrical scanning SAR systems with linear sparse MIMO arrays. Many application scenarios have extremely high requirements for imaging speed and quality, making it urgent 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 composite arrays. However, their actual implementation computational complexity is too high, hindering their application in real-time imaging systems. Most spatial wavenumber domain imaging methods are designed for uniform arrays. Although some research literature has proposed methods suitable for non-uniform sparse arrays, such as the distance migration method based on non-uniform fast Fourier transform (NUFFT-based-RMA), their imaging quality and computational efficiency are not high as the array sparsity increases, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system to address the problems in related technologies, such as the need to improve imaging speed of traditional imaging methods and range migration methods based on non-uniform fast Fourier transform, and the limitations in imaging quality and computational efficiency as the array sparsity increases.

[0006] The first aspect of this 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 synthetic array in a target linear sparse MIMO array cylindrical scanning SAR system, and dividing the complete synthetic array into multiple sub-arrays to select local echo data corresponding to the multiple sub-arrays 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 based on the local echo data; and 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.

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

[0008] Optionally, in one embodiment of this application, before reconstructing the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each of the plurality of 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 spectral analysis results of the target linear sparse MIMO array cylindrical scanning SAR system, to analyze the local spectral support domain of the image at any location; analyzing the approximate minimum volume bounding box of the local spectral support domain, to convert the actual bounding box of the local spectral 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 the limited support range of the local spectrum; and calculating a uniform sampling grid with a uniform sampling rate in the (u,v,n) coordinate system for each sub-array based on the limited support range, 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 this application, the formula for calculating the uniform sampling grid with a uniform sampling rate in the (u,v,n) coordinate system for each subarray is:

[0010]

[0011]

[0012] Where 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 represents the maximum and minimum wavenumber values, respectively; x0, y0, and z0 are the parameters in (x0, y0, z0), where (x0, y0, z0) represents the slowly changing stationary point; R represents the system's scan radius; z t,ζ,SA With z r,ζ,SA These represent the transmit and receive antennas closest to z0 in the transmit and receive subarrays, respectively. t,min,SA z t,max,SA z r,min,SA z r,max,SA θ represents the minimum and maximum z-coordinates of the receiving subarray and the transmitting subarray, respectively; A1, A2 and B1, B2, B3, B4 are transitional parameters designed to facilitate the calculation process; ζ,SA θ represents the subarray scanning angle corresponding to the minimum difference in distance between the target point and the projection points of each antenna in the xoy plane; min,SA With θ max,SA θ represents the minimum and maximum scan angles of the subarray. mid,SA It is the midpoint of the subarray scanning angle.

[0013] Optionally, in one embodiment of this application, the step of superimposing the sub-images of each sub-array according to a preset superposition rule to obtain a three-dimensional image of the target linear sparse MIMO array cylindrical scanning SAR system includes: performing 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 grids of the corresponding level of each sub-array to perform pairwise coherent superposition of the sub-images of each sub-array to obtain multiple sub-image pairs, and repeating 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 this application, the expression for the space-time 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 sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th sub-image, j represents the complex exponential term; Φ is a spatially varying function of different locations (x, y, z) in space and the frequency of the transmitted signal, k max k min These represent the maximum and minimum wavenumber values, respectively. A1, A2, B1, B2, B3, and B4 are transitional parameters designed to facilitate the calculation process.

[0018] A second aspect of this 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 synthetic array in a target linear sparse MIMO array cylindrical scanning SAR system, and dividing the complete synthetic array into multiple sub-arrays to select local echo data corresponding to the multiple sub-arrays from the complete echo data; a reconstruction module for reconstructing a sub-image corresponding to each sub-array in a non-uniform sampling grid corresponding to each sub-array based on the local echo data; and an imaging module for 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.

[0019] Optionally, in one embodiment of this application, the acquisition module includes: an acquisition unit, configured to acquire the raw echo data of the complete integrated array in the target linear sparse MIMO array cylindrical scan SAR system; and a correction unit, configured to correct the raw echo data based on the amplitude inconsistency, time delay inconsistency, and amplitude imbalance of the multiple channel signals at different frequencies in the target linear sparse MIMO array cylindrical scan SAR system, so as to obtain the complete echo data.

[0020] Optionally, in one embodiment of this application, it further includes: a simulation module, used to simulate the spectral characteristics of the image of the target linear sparse MIMO array cylindrical scan SAR system based on the local spectral analysis results of the target linear sparse MIMO array cylindrical scan 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 plurality of sub-arrays, so as to analyze the local spectral support domain of the image at any location; an analysis module, used to analyze the approximate minimum volume bounding box of the local spectral support domain, so as to convert the actual bounding box of the local spectral support domain into a unit box at the origin of the spatial wavenumber domain of the target linear sparse MIMO array cylindrical scan SAR system, thereby obtaining the limited support range of the local spectrum; and a calculation module, used to calculate the uniform sampling grid with a uniform sampling rate in the (u,v,n) coordinate system for each sub-array, 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 this application, the calculation formula for the uniform sampling grid with a uniform sampling rate in the (u,v,n) coordinate system for each subarray can be expressed as:

[0022]

[0023] Where 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 represents the maximum and minimum wavenumber values, respectively; x0, y0, and z0 are the parameters in (x0, y0, z0), where (x0, y0, z0) represents the slowly changing stationary point; R represents the system's scan radius; z t,ζ,SA With z r,ζ,SA These represent the transmit and receive antennas closest to z0 in the transmit and receive subarrays, respectively. t,min,SA z t,max,SA z r,min,SA z r,max,SA θ represents the minimum and maximum z-coordinates of the receiving subarray and the transmitting subarray, respectively; A1, A2 and B1, B2, B3, B4 are transitional parameters designed to facilitate the calculation process; ζ,SA θ represents the subarray scanning angle corresponding to the minimum difference in distance between the target point and the projection points of each antenna in the xoy plane; min,SA With θ max,SA θ represents the minimum and maximum scan angles of the subarray. mid,SA It is the midpoint of the subarray scanning angle.

[0024] Optionally, in one embodiment of this application, the imaging module includes: a processing unit, configured to perform spatial downconversion processing on the sub-images of each subarray to obtain compressed sub-images; a superposition unit, configured to interpolate the compressed sub-images onto adjacent higher-level sampling grids corresponding to each subarray level, to perform pairwise coherent superposition of the sub-images of each subarray 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 this application, the expression for the space-time 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 sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th sub-image, j represents the complex exponential term; Φ is a spatially varying function of different locations (x, y, z) in space and the frequency of the transmitted signal, k max k min These represent the maximum and minimum wavenumber values, respectively. A1, A2, B1, B2, B3, and B4 are transitional parameters designed to facilitate the calculation process.

[0029] A third aspect of this application provides an electronic device, including: 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 a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system as described in the above embodiments.

[0030] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system described above.

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

[0032] This application's embodiments can divide the complete integrated array into multiple subarrays, reconstruct downsampled sub-images of each subarray, and then obtain near-field 3D imaging of the target linear sparse MIMO array cylindrical scanning SAR system by superimposing the sub-images. This achieves innovative derivation of analytical expressions for SDC and LLT by analyzing local spectral characteristics, using them for spectral compression of sub-images to reduce the number of sampling points, overcoming the high time and computational complexity problems of traditional time-domain imaging methods. Furthermore, by using time-domain imaging methods to reconstruct downsampled sub-images of each subarray and obtaining subsequent levels of sub-images through pairwise merging, accurate 3D reconstruction results are finally obtained, achieving accurate and efficient 3D imaging of arbitrary linear sparse MIMO array cylindrical scanning SAR systems. This solves the problems in related technologies, such as the need for improvement in imaging speed of traditional imaging methods and range migration methods based on non-uniform fast Fourier transform, and the limitations in imaging quality and computational efficiency that arise with increasing array sparsity.

[0033] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

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

[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 this 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 an embodiment of this application;

[0037] Figure 3 This is a schematic diagram of the convex hull of the local spectral support domain of a sub-image and key points and minimum volume bounding boxes under different linear sparse MIMO arrays according to an embodiment of this application.

[0038] Figure 4 This is a schematic diagram of the support domain after the sub-image spectrum has undergone SDC and LLT transformations according to an embodiment of this application.

[0039] Figure 5 A flowchart of a three-dimensional fast near-field imaging method for a linear sparse MIMO array cylindrical scanning SAR system according to an embodiment of this application;

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

[0041] Figure 7 This is a schematic diagram of the near-field imaging device of the sparse MIMO array cylindrical scanning SAR system provided in the embodiments of this application;

[0042] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0043] Figure label:

[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 Implementation

[0045] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0046] The near-field imaging method of a sparse MIMO array cylindrical scanning SAR system according to embodiments of this application is described below with reference to the accompanying drawings. Traditional imaging methods and range migration methods based on non-uniform fast Fourier transform mentioned in the background art still have room for improvement in imaging speed, and with the increase of array sparsity, imaging quality and computational efficiency also have certain limitations. This application provides a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system. In this method, the complete integrated array is divided into multiple sub-arrays, downsampled sub-images of each sub-array are reconstructed, and then the sub-images are superimposed to obtain a near-field three-dimensional image of the target linear sparse MIMO array cylindrical scanning SAR system. This breakthrough innovatively derives analytical expressions for SDC and LLT by analyzing local spectral characteristics, enabling spectral compression of sub-images and reducing the number of sampling points. This overcomes the high time and computational complexity issues of traditional time-domain imaging methods. In practice, time-domain imaging methods are used to reconstruct downsampled sub-images for each subarray, and subsequent sub-images are obtained through pairwise merging, ultimately yielding accurate 3D reconstruction results. This achieves accurate and efficient 3D imaging of arbitrary linear sparse MIMO array cylindrical scanning SAR systems. Furthermore, this addresses the limitations of traditional imaging methods and range migration methods based on non-uniform fast Fourier transform in imaging speed, and the constraints on imaging quality and computational efficiency that arise with increasing array sparsity.

[0047] Before explaining the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system in the embodiments of this application, Table 1 is a formula parameter table of one embodiment of this application to facilitate understanding of this application by those skilled in the art. Table 1 explains the parameter symbols involved in this application. Table 1 can represent, but is not limited to, the following:

[0048]

[0049]

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

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

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

[0053] In step S101, complete echo data of the complete integrated array in the 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 multiple sub-arrays from the complete echo data.

[0054] Understandably, a 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, sparse array means that the antenna elements are not uniformly distributed on a straight line. By optimizing the spacing between array elements, redundant sampling is reduced, system complexity and cost are reduced, and the number of array elements can be reduced compared with traditional uniform array.

[0056] Cylindrical scanning refers to a radar system scanning a cylindrical trajectory around the target area (usually the radar platform rotates around the target, or the target rotates on a rotating platform), forming a 360° or wide-angle observation. By combining range (radial) and azimuth (circumferential) signal processing, three-dimensional imaging of the target can be achieved.

[0057] In some embodiments, after acquiring the complete echo data of the target linear sparse MIMO array cylindrical scanning SAR system, the embodiments of this application can select local echo data corresponding to multiple subarrays from the complete echo data according to the multiple subarrays divided by the complete integrated array, so as to reconstruct the sub-image corresponding to each subarray based on the local echo data.

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

[0059] In a sparse MIMO array cylindrical scanning SAR system, the differences in feed line length, amplifier gain, and RF link devices (such as filters) among the antenna elements lead to variations in amplitude attenuation and time delay in the signal transmission channels corresponding to each antenna element. Inconsistent amplitudes across multiple channels result in deviations in echo energy distribution, while inconsistent time delays disrupt signal phase coherence, ultimately affecting the shape reconstruction accuracy of the object under test, reducing the resolution and contrast of the SAR image, and consequently impacting image quality.

[0060] In some embodiments, when acquiring complete echo data of a target linear sparse MIMO array cylindrical scanning SAR system, this application needs to process the original echo data of the complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system. This mainly involves correcting the amplitude inconsistencies, time delay inconsistencies, and amplitude imbalances between different frequencies of multiple channel signals in the target linear sparse MIMO array cylindrical scanning SAR system. That is, the original echo data is subjected to amplitude weighting and time delay adjustment on a channel-by-channel and frequency-by-frequency basis. Only after correction can the original echo data be considered complete echo data.

[0061] The methods for correcting amplitude inconsistencies, time delay inconsistencies, and amplitude imbalances between multiple channel signals at different frequencies can be selected or adjusted by those skilled in the art based on actual circumstances. The embodiments in this application are merely illustrative and do not impose specific limitations.

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

[0063] Step S102: Based on the local echo data, reconstruct the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each sub-array in multiple sub-arrays.

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

[0065] The reconstruction method for the sub-images can be, but is not limited to, the BPA (Back Projection Algorithm) method.

[0066] The reconstruction process of the sub-images in the embodiments of this application will be further explained below.

[0067] Optionally, in one embodiment of this application, before reconstructing the sub-image corresponding to each sub-array in the non-uniform sampling grid corresponding to each sub-array in 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 spectral analysis results of the target linear sparse MIMO array cylindrical scanning SAR system, to analyze the local spectral support domain of the image at any location; analyzing the approximate minimum volume bounding box of the local spectral support domain, to convert the actual bounding box of the local spectral 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 the constrained support range of the local spectrum; and calculating a uniform sampling grid with a uniform sampling rate in the (u,v,n) coordinate system for each sub-array based on the constrained support range, 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 in the (u,v,n) coordinate system for each sub-array can be, but is not limited to, expressed as:

[0068]

[0069]

[0070] Where u(x,y,z), v(x,y,z), and n(x,y,z) represent the transformed (u,v,n) coordinates, respectively; k max k minrepresents the maximum and minimum wavenumber values, respectively; x0, y0, and z0 are the parameters in (x0, y0, z0), where (x0, y0, z0) represents the slowly changing stationary point; R represents the system's scan radius; z t,ζ,SA With z r,ζ,SA These represent the transmit and receive antennas closest to z0 in the transmit and receive subarrays, respectively. t,min,SA z t,max,SA z r,min,SA z r,max,SA θ represents the minimum and maximum z-coordinates of the receiving subarray and the transmitting subarray, respectively; A1, A2 and B1, B2, B3, B4 are transitional parameters designed to facilitate the calculation process; ζ,SA θ represents the subarray scanning angle corresponding to the minimum difference in distance between the target point and the projection points of each antenna in the xoy plane; min,SA With θ max,SA θ represents the minimum and maximum scan angles of the subarray. mid,SA It is the midpoint of the subarray scanning angle.

[0071] Those skilled in the art will understand that near-field imaging methods applicable to linear sparse MIMO array cylindrical scanning SAR systems struggle to simultaneously guarantee imaging accuracy and computational efficiency. Therefore, this application's embodiments start with the local spectral characteristics of near-range radar images, utilizing spatial down-conversion (SDC) and local linear transformation (LLT) of the linear MIMO array cylindrical scanning SAR system to achieve spectral compression. Then, a temporal imaging method is used to reconstruct the downsampled sub-images of each subframe, thereby generating a complete SAR three-dimensional image from the sub-images.

[0072] As one possible approach, this application first analyzes the spectral support range characteristics (spectral properties) 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 of the spectrum in the wavenumber domain), the spatially variable characteristics of the SAR image spectrum are analytically represented. That is, mathematical analytical expressions are used to describe the changes in the shape, range, and boundary contour of the wavenumber domain spectral support region of the SAR system during imaging as a function of spatial location (such as the target location or radar platform location).

[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 this application. Figure 2 As shown, a one-dimensional linear MIMO array can be mechanically scanned with a fixed radius R, thus forming a cylindrical synthetic aperture. The radar transmitting and receiving elements are discretely distributed on a straight line perpendicular to the xoy plane, with position coordinates p, p', and p''. t=(Rcosθ,Rsinθ,z) t ), p r =(Rcosθ,Rsinθ,z) r ), where p t and p r These represent the coordinates of the transmitting and receiving elements within 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 x-axis; and z... t With z r The height coordinates of the transmitting and receiving elements are respectively. The scattering characteristics of the target are f(x,y,z), which is the target function that the millimeter-wave near-field imaging system needs to reconstruct. x, y, and z represent the three-dimensional coordinate parameters in the Cartesian coordinate system (x,y,z).

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

[0075]

[0076] Where s represents the echo signal; k represents the number of echoes, k = 2πf / c, where 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 Let be the distances from the transmitting element and the receiving element to the target point in space, respectively, which can be expressed as follows:

[0077]

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

[0079]

[0080] In formula (4), BPA reconstructs the imaging result pixel by pixel by performing spatial wavenumber integration at each location within the imaging area. The computational cost 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 near-range SAR images, the spectral characteristics, including carrier frequency and bandwidth, exhibit strong spatial variability.

[0081] Therefore, embodiments of this application can perform local spectrum analysis, and then, based on the results of the local spectrum analysis, simulate the spectral characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system, so as to analyze the local spectral support domain (the region of the sub-image with non-zero values ​​in the spectral space) of the image at any location.

[0082] First, the spatial wavenumber domain representation of images from a linear sparse MIMO array cylindrical scanning SAR system can be directly calculated using a three-dimensional Fourier transform, i.e.:

[0083]

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

[0085] By approximating the triple integral of equation (5) with respect to x, y, and z using the principle of multidimensional stationary phase, we can obtain:

[0086]

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

[0088]

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

[0090]

[0091] Substituting and solving, we obtain any k = k0 = (k x ,k y ,k z The expression for ) can be represented 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 These represent the corresponding terms in the spatial wavenumber domains for x, y, and z, respectively.

[0094] Equation (9) determines the relationship between the spatial wavenumber domain variables and system parameters of a linear MIMO array cylindrical scanning SAR imaging system. When the pixel points (x0, y0, z0) in the imaging interval are fixed, for any wavenumber domain variable k x k y k z Only when the system parameters k, θ, z tz r Only when formula (9) is satisfied can the asymptotic integral result of formula (6) be non-zero, which is a set of a finite number of spatial wavenumber domain scatter points.

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

[0096]

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

[0098] Therefore, the spectral support range of the SAR imaging results can be determined, and can be expressed, but is not limited to, as follows:

[0099]

[0100] Among them, SP A The spectral support domain of the imaging result is represented by D, where D is the imaging region containing the imaging target, A represents the MIMO array aperture, and k min k max Let p0 and p0 represent the minimum and maximum wavenumbers, respectively. For any location p0∈D in the imaging region, the corresponding spectral support range can be calculated, and the expression can be, but is not limited to, as follows:

[0101]

[0102] in, This represents the spectral support domain at any location p0 in the imaging region.

[0103] In this embodiment, it can be defined as the local spectral support domain at position p0. According to the Carson bandwidth criterion, the bandwidth of the global image's spectral support domain is approximately equal to the union of the local spectral support domains at each location within the imaging region, i.e.:

[0104]

[0105] Equations (9)-(13) describe the support domain characteristics of the spectrum of a cylindrical scan SAR image from a linear sparse MIMO array. Next, embodiments of this application can correspondingly derive the Nyquist sampling rate for each dimension, expressed, but not limited to, as:

[0106]

[0107] Where, k x min k x max k y min k y max k z min and k z max They represent k as determined by formula (9). x k y and k z The lower and upper limits.

[0108] Therefore, it can be seen that the imaging results of 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 location can be analyzed. Based on this, embodiments of this application can design an efficient spectral compression method combining decomposition techniques based on the spectral support domain of the image at any location, thereby reducing sampling requirements and thus reducing the computational load of time-domain image reconstruction methods.

[0109] Specifically, embodiments of this application can analyze the approximate minimum volume bounding box of the local spectral support domain to convert the actual bounding box of the local spectral 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 limited support range of the local spectrum. This limited support range can effectively reduce the aperture range, thereby reducing the sampling amount and thus reducing the computational complexity.

[0110] First, embodiments of this application can analyze and derive the approximate minimum volume bounding box (used to enclose the geometry of the local spectral support domain) based on the spectral support domain of an image at any location.

[0111] Figure 3 This is a schematic diagram of the convex hull of the local spectral support domain of a sub-image and the bounding boxes of key points and minimum volume under different linear sparse MIMO arrays according to an embodiment of this application. Figure 3 As shown, the outline of the local spectral support domain is an irregular three-dimensional body with a smooth curved surface, a concave bottom, a convex middle, and a large span of scanning angle dimensions.

[0112] To maximize the spectral occupancy of the sub-image after local spectral transformation, embodiments of this application may, but are not limited to, use key points in the spatial wavenumber domain to represent the approximate minimum volume bounding box of the local spectrum at any location p in the imaging region of the sub-array SA. Its definition can be expressed as follows:

[0113]

[0114] Where, θ ζ,SAThe subarray scanning angle represents the subarray scanning angle where the difference in distance between the target point and the projection points of each antenna in the xoy plane is minimized. It is calculated as follows: for the radius R of the circle containing the antennas, the subarray scanning angle is θ. scan,SA Let each scan angle θ i ∈θ scan,SA The antenna coordinate projection on the xoy plane is (Rcosθ) i ,Rsinθ i ), and calculate the Euclidean distance d between the target position (x0, y0) in the projection point domain. i :

[0115]

[0116] If all distances are consistent, then the distance from the target to all scanning angles of the subarray is considered to be almost identical, and in this case, θ ζ,SA Take the median value of the scanning angles; otherwise, select the angle that minimizes the target distance from all scanning angles as θ. ζ,SA The definition is as follows:

[0117]

[0118] In addition, z t,min,SA z t,max,SA z r,min,SA z r,max,SA represents the minimum and maximum z-coordinates of the receiving subarray and the transmitting subarray, respectively. max Defined as maximum wavenumber, θ min,SA With θ max,SA This represents the minimum and maximum scan angles of the subarray. t,ζ,SA With z r,ζ,SA Let z and z0 represent the transmit and receive antennas closest to z0 in the transmit and receive subarrays, respectively, and they can be defined as follows:

[0119]

[0120] The key points defined by formula (15) characterize the orientation information of the local spectral 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, this embodiment of the application also needs to further determine the boundary points of the distance dimension. Considering that the nearest and farthest points of the support domain in the distance direction are not the same for different MIMO linear array transceiver elements, this embodiment of the application can be analyzed in three cases, but not limited to the following.

[0122] I. In the case of separate transmit and receive antenna distribution, the farthest point in the range direction of the support domain is determined by the transmit and receive antennas with the smallest distance, while the nearest point is contributed by the transmit and receive antennas with the largest distance. When the scanning angle increases in the azimuth direction, the nearest point in the support domain is also affected by the boundary points of the scanning angle. Therefore, the key points in the range direction of the support domain in the corresponding three-dimensional wavenumber domain can be represented, but are not limited to, as follows:

[0123]

[0124] Where, θ mid,SA k is the midpoint of the subarray scanning angle. min This is the minimum wavenumber. Furthermore, formula (20) addresses the case where all transmitting element coordinates are less than the receiving element coordinates. When all transmitting element positions are greater than the receiving element position coordinates, the z-coordinates of k5 and k6... t,min,SA With z t,max,SA z r,max,SA With z r,min,SA They should exchange positions.

[0125] II. In the case of intersecting transmit and receive antennas, the selection of the closest point in the support domain distance direction is closely related to the target point's location. Specifically, when the target and the two ends of the transmit and receive antenna array z... t,min,SA z r,max,SA The sum of its distances is less than its distances to the other two ends z. t,max,SA z r,min,SA When the sum of distances is equal to the sum of distances, the nearest point in the support region is considered to be located at z. t,min,SA z r,max,SA The transmit and receive antennas at the location contribute together; otherwise, the nearest point is determined by z. t,max,SA z r,min,SA The decision is made. Furthermore, considering the influence of the scanning angle, the average of the maximum and minimum scanning angles of the sub-array is taken; therefore, k5 can be determined, but is not limited to, by the following formula:

[0126]

[0127] Where γ1 represents the transmit / receive antenna 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 ) represent the Euclidean distances to spatial points (x0, y0, z0) respectively, and γ2 represents the transmit and receive antenna array 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 point (x0, y0, z0) respectively.

[0128] When calculating the farthest point of the support domain, considering that the wavenumber domain bandwidth of a near-field MIMO radar image can reach up to 2k in the range direction when the imaging range is large enough to include all near-field regions,... max However, if we directly consider the key point k6, which is the furthest point, as having a modulus of 2k... max If the vector with direction k5 / |k5| is a vector, then the minimum volume bounding box of the spectral support domain will contain a large area of ​​blank region, which will ultimately reduce the efficiency of spectral compression. At the same time, it will cause the k6 vector to be a rotational field, and subsequent processing will require additional derotation transformation, which increases the computational complexity.

[0129] Therefore, in this embodiment, an approximation is used for calculating the farthest point of the support domain: let z be the distance from the target point to the two endpoints of the transmitting antenna array. t,min,SA z t,max,SA Let α be the included angle formed by the two lines connecting the two points. Find the angle bisector λ. t The position of the transmitting antenna closest to the intersection of the line with the transmitting array is the corresponding z-axis. t,ξ,SA .

[0130] Similarly, let z be the distance from the target point to the two endpoints of the receiving antenna array. r,min,SA z r,max,SA Let β be the included angle formed by the two lines connecting them, and find its angle bisector λ. t The position of the receiving antenna closest to the intersection of the line with the receiving array is the corresponding z-axis. r,ξ,SA .

[0131] Using this method, the embodiments of this application can accurately and effectively approximate the furthest point of the support domain in the distance direction, while avoiding the introduction of too many blank areas and additional derotation field calculations within the minimum volume bounding box. In this case, k6 can be determined, but is not limited to, by 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] Third, in the case of the transmitting and receiving antennas being distributed, the selection strategy for the farthest and nearest points is basically the same as that for the intersecting distribution. However, due to the change in the antenna arrangement, the corresponding "nearest antenna pair" and "farthest antenna pair" will also change when the target point is in different positions. Therefore, the key points k5 and k6 in the distance direction are still determined by formulas (21) and (22).

[0134] Geometrically, the local spectral support domains in the above three cases exhibit an azimuth orientation k y With k z It typically exhibits a fan-shaped or curved appearance, in the distance k direction. x The arc transition is influenced by factors such as antenna distribution and target location. 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 this 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 spectrum compression effect.

[0135] SDC is achieved by multiplying the sub-image in the spatial domain with the phase term of the spatial variable, assuming f m,n For the imaging result of any subarray, the operating formula of SDC can be expressed, but is not limited to, as follows:

[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 sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th sub-image, where Φ is also a spatially varying function of different locations (x, y, z) and the transmitted signal frequency, and its gradient should be the center k of the local spectral support domain. c That is, satisfying:

[0138]

[0139] k here c Key points in the spatial wavenumber domain can be represented, but are not limited to, as follows:

[0140]

[0141] Depending on the arrangement of the MIMO array, when the transmitting and receiving elements of the MIMO array are completely separated in space, its k c The scalar potential function Φ can be, but is not limited to, expressed as:

[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 all coordinates of the transmitting element are less than or equal to the coordinates of the receiving element. When the position of the transmitting element is greater than the position coordinates of the receiving element, the z in A1 and A2... t,max,SA z r,min,SA They should be replaced with z respectively. t,min,SA z r,max,SA z of 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.

[0146] When the transmitting 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, A1 and A2 in formula (27) still need to be updated to formula (28), and the z-values ​​of B1, B2, B3, and B4 also need to be updated. t,min,SA With z t,max,SA z r,max,SA With z r,min,SA They should exchange positions.

[0149] Next, embodiments of this application can perform downconversion operations on a local spectrum. Figure 4 This is a schematic diagram of the support domain after the sub-image spectrum has undergone SDC and LLT transformations according to an embodiment of this application. Figure 4 As shown in (a) and (b), after SDC, the local spectral support domain is moved to the origin of the spatial wavenumber domain, thereby significantly reducing the sampling rate required for the sub-image.

[0150] Subsequently, embodiments of this application can utilize LLT to convert the bounding box of the local spectral support domain into a unit box at the origin of the spatial wavenumber domain, thereby further improving the spectral occupancy rate. The formulas for the three generating vectors v1, v2, and v3 of the parallelogram of the local spectral support domain can be, but are not limited to, expressed as:

[0151]

[0152] Linear transformations (including scaling, rotation, and shearing transformations) used in the spatial wavenumber domain for the local spectral support domain are incorporated into the linear coordinate transformation matrix T. k In the space wavenumber domain, the LLT operation can be represented as:

[0153] 2πE=T k [v1 v2 v3] (30)

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

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

[0156] T k =2π[v1 v2 v3] -1 (31)

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

[0158]

[0159] Furthermore, in order to implement LLT for sub-images in the spatial domain, embodiments of this application calculate a uniform sampling grid with a uniform sampling rate for each sub-array in the UVN coordinate system (u,v,n), thus requiring the establishment of a coordinate transformation. And it is locally linearized into the desired local linear transformation, which can be expressed by, but is not limited to, the following formula:

[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, but are not limited to, as follows:

[0162]

[0163] Because different MIMO array arrangements result in different definitions of the farthest and nearest points in the spectral support domain in the distance direction, the vector field v3 is different, which leads to different solutions for n(x,y,z).

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

[0165]

[0166] Similarly, formula (36) only gives the case where all the coordinates of the 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 position coordinate of the receiving array element, the z coordinates of A1 and A2 in n(x,y,z) are different. t,max,SA z r,min,SA The variables should be replaced with z respectively. t,min,SA z r,max,SA Variables, 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 and receiving elements of a MIMO array intersect or are contained within each other, if γ1≤γ2, A1 and A2 in formula (36) should be updated to the values ​​in formula (28); if γ1>γ2, A1 and A2 in formula (36) also need to be updated to the values ​​in formula (28), and the z-values ​​of B1, B2, B3, and B4 should also be updated accordingly. t,min,SA With z t,max,SA z r,max,SA With z r,min,SA Their positions should be exchanged. Thus, the required local linear coordinate transformation relationship in the spatial domain is determined by equations (34) to (36).

[0168] The support range of the local spectrum after SDC and LLT, i.e., the limited support range of the local spectrum in the embodiments of this application, is shown in the appendix. Figure 4 As shown in (c), this is the spectral distribution within the unit frame at the origin in the spatial wavenumber domain.

[0169] After the above processing, in practical applications, the embodiments of this application can perform SDC down-conversion operation on the local spectrum of the target linear sparse MIMO array cylindrical scanning SAR system using formula (23), and then perform 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, thereby enabling sampling on the uniform sampling grid and reconstructing the sub-image of each subarray with the fewest sampling points.

[0170] Then, embodiments of this application can convert the uniform sampling network into a non-uniform sampling grid in the (x,y,z) coordinate system, thereby reconstructing the sub-image corresponding to each sub-array in the non-uniform grid corresponding to each sub-array.

[0171] The embodiments of this application can analyze the shape of the wavenumber domain spectrum at different locations in space, analytically represent the spatial variation 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 compress the spectrum of sub-images, thereby improving the spectrum occupancy rate and reducing the number of sampling points. This effectively solves the problem of complex numerical pre-computation required for spatial downconversion (SDC) and local linear transformation (LLT) operations during spectrum compression.

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

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

[0174] Here, the preset overlay rule can be understood as the pre-set overlay rule when multiple sub-images are overlaid 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 overlaid until the final SAR three-dimensional imaging result is obtained.

[0175] Optionally, in one embodiment of this application, sub-images of each subarray are superimposed according to a preset superposition rule to obtain a three-dimensional image of the target linear sparse MIMO array cylindrical scanning SAR system. This includes: performing spatial downconversion processing on the sub-images of each subarray to obtain compressed sub-images; interpolating the compressed sub-images onto adjacent higher-level sampling grids corresponding to each subarray level to perform pairwise coherent superposition of the sub-images of each subarray, obtaining multiple sub-image pairs, and repeating 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 spatial downconversion 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 sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th sub-image, j represents the complex exponential term; Φ is a spatially varying function of different locations (x, y, z) in space and the frequency of the transmitted signal, kmax k min These represent the maximum and minimum wavenumber values, respectively. A1, A2, B1, B2, B3, and B4 are transitional parameters designed to facilitate the calculation process.

[0179] Based on the descriptions of other embodiments, it will be understood that this application can superimpose the sub-images of each sub-array according to certain superposition rules to obtain the final three-dimensional image.

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

[0181] Then, in this embodiment of the 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] Next, the new sub-image pairs are coherently superimposed as new sub-images, and this coherent superposition process is repeated until a unique sub-image pair is obtained. This unique sub-image is the final SAR three-dimensional imaging result.

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

[0184] In some embodiments, during the process of superimposing sub-images according to certain superposition rules, an interpolation process is included, in which the sub-images of each sub-array are interpolated to the sampling grid of the adjacent higher level of the level of each sub-array (the level of each sub-array before superposition is used as the level reference, and the level to which the superimposed sub-image belongs is called the adjacent higher level), thereby realizing the pairwise coherent superposition of the sub-images of each sub-array on the sampling grid of the adjacent higher level.

[0185] To avoid aliasing during the interpolation process, this embodiment of the application can first perform spatial downconversion processing on the sub-images of each subarray to convert the sub-images of each subarray into compressed sub-images. The expression for spatial downconversion processing is the same as that in 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 sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th sub-image, j represents the complex exponential term; Φ is a spatially varying function of different locations (x, y, z) in space and the frequency of the transmitted signal, k max k min These represent the maximum and minimum wavenumber values, respectively. A1, A2, B1, B2, B3, and B4 are transitional parameters designed to facilitate the calculation process.

[0189] After converting the sub-image pair into a compressed sub-image, the embodiments of this application can interpolate the compressed sub-image to a sampling grid one level higher than the sub-array level where the sub-image pair was located before superposition. Then, the carrier of the compressed new sub-image is recovered by multiplying the sub-image with the conjugate space-variable down-conversion function, ensuring the continuous accumulation of the sub-images, and obtaining the processed sub-image.

[0190] Finally, the sub-images after coherent superposition on adjacent higher-level sampling grids are used to obtain sub-image pairs. These sub-image pairs are then used as new sub-images, and the same transformation, interpolation, and coherent superposition processes are applied to them until the final 3D imaging result is obtained.

[0191] Additionally, under the superposition rule of coherent superposition in pairs, when dividing a complete synthetic array into multiple subarrays, those skilled in the art may, but are not limited to, first determine the decomposition level and then divide the complete synthetic array into multiple subarrays based on the decomposition level, because the decomposition level is related to the computation speed and interpolation error.

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

[0193] To improve computational accuracy, the decomposition level can be appropriately reduced, that is, the number of subarrays in the complete synthetic array can be reduced, thereby reducing the number of interpolations and reducing interpolation errors. To improve computational speed, the decomposition level can be appropriately increased, that is, the number of subarrays in the complete synthetic array can be increased.

[0194] It should be noted that the specific decomposition level can be set or adjusted by those skilled in the art according to the actual situation and needs. The embodiments in this application are only illustrative and do not impose any specific limitations.

[0195] The present application will be explained in detail below with reference to a specific embodiment.

[0196] Figure 5 This is a flowchart illustrating a three-dimensional fast near-field imaging method applicable to a linear sparse MIMO array cylindrical scanning SAR system, according to one embodiment of this application. Figure 5 As shown, the method includes the following steps:

[0197] Step 1: In this embodiment of the application, the original echo dataset is first obtained, and then the inconsistencies in amplitude and time delay between different channel signals and the amplitude imbalance between different frequencies of each channel signal are corrected to obtain complete echo data s(k,θ,z). t ,z r );

[0198] Step 2: For a decomposition level of L, the complete synthetic array (synthetic aperture) can be divided into 2. L-1 Each subarray is used, and the corresponding subarray SA is extracted from the complete echo data. 1,n Echo data;

[0199] Step 3: Calculate the uniform sampling grid with a uniform sampling rate for each level 1 subarray (the lowest level subarray) in the UVN coordinate system (u,v,n) using LLT, and convert it into a non-uniform sampling grid G ​​in the (x,y,z) coordinate system. 1,n Where (u,v,n) are the coordinates 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 subarray on the sampling grid corresponding to each subarray, i.e., the first-level sub-image f. 1,n At this point, due to the reduction in aperture, the resolution of the sub-image decreases, causing the sub-image to appear smaller within the sampling grid G. 1,n The sampling rate is reduced, thereby alleviating the overall computational burden;

[0201] Step 5: Level 1, 2 L-1 Sub-images form 2 L-2 Each sub-image pair contains sub-image f 1,2n-1 and f 1,2n The second-order sub-image f is obtained by coherent superposition. 2,n ;

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

[0203] Interpolate these (m-1)th level sub-images with well-defined spectral support ranges to the m-th level sampling grid G. m,n Then, the carrier wave is recovered by multiplying the sub-image with the conjugate spatially variable downconversion function, ensuring the coherent accumulation of the sub-image:

[0204] f m,n (p)=f' m,n (p)e jΦ (39))

[0205] Then, the processed (m-1)th level sub-images are coherently superimposed using the formula to obtain G. m,n The upsampled m-th 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 within the imaging region, and G m,n This represents the m-th level of non-uniform sampling grid in (x,y,z) coordinates.

[0208] Repeat this process to obtain the final imaging result, 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 three-dimensional imaging results according to an embodiment of this application, as shown below. Figure 6 As shown, the BPA method, which serves as the benchmark, has a computational complexity of O(N). 5 The near-field imaging method of the sparse MIMO array cylindrical scanning SAR system in this embodiment can achieve almost the same imaging quality as the baseline back projection method (BPA), but only requires O(N) to maintain the imaging quality. 3 With a computational complexity of log N), accurate and efficient near-field three-dimensional imaging of an arbitrary sparse linear MIMO array cylindrical scanning SAR system is achieved.

[0210] The near-field imaging method for a sparse MIMO array cylindrical scanning SAR system proposed in this application involves dividing the complete integrated array into multiple sub-arrays, reconstructing downsampled sub-images of each sub-array, and then superimposing the sub-images to obtain a near-field three-dimensional image of the target linear sparse MIMO array cylindrical scanning SAR system. This achieves the innovative derivation of analytical expressions for SDC and LLT by analyzing local spectral characteristics, utilizing these expressions for spectral compression of sub-images to reduce the number of sampling points, thus overcoming the high time and computational complexity of traditional time-domain imaging methods. In practice, time-domain imaging methods are used to reconstruct downsampled sub-images of each sub-array, and subsequent levels of sub-images are obtained through pairwise merging, ultimately yielding accurate three-dimensional reconstruction results. This achieves accurate and efficient three-dimensional imaging of arbitrary linear sparse MIMO array cylindrical scanning SAR systems. Therefore, this method addresses the limitations of traditional imaging methods and range migration methods based on non-uniform fast Fourier transform in related technologies, which still require improvement in imaging speed, and whose imaging quality and computational efficiency become limited with increasing array sparsity.

[0211] Next, referring to the accompanying drawings, a near-field imaging apparatus for a sparse MIMO array cylindrical scanning SAR system according to an embodiment of this application is described.

[0212] Figure 7 This is a schematic diagram of the near-field imaging device of the sparse MIMO array cylindrical scanning SAR system according to an embodiment of this 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] The acquisition module 100 is used to acquire the complete echo data of the 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 the local echo data corresponding to multiple sub-arrays from the complete echo data.

[0215] The reconstruction module 200 is used to reconstruct the sub-image corresponding to each subarray in the non-uniform sampling grid corresponding to each subarray in multiple subarrays based on 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 the three-dimensional imaging results of the target linear sparse MIMO array cylindrical scanning SAR system.

[0217] Optionally, in one embodiment of this application, the acquisition module 100 includes: an acquisition unit for acquiring the raw echo data of the complete integrated array in the target linear sparse MIMO array cylindrical scanning SAR system; and a correction unit for correcting the raw echo data based on the amplitude inconsistency, time delay inconsistency, and amplitude imbalance of 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 this 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 spectral 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 multiple sub-arrays, so as to analyze the local spectral support domain of the image at any location.

[0220] The analysis module is used to analyze the approximate minimum volume bounding box of the local spectral support domain, so as to convert the actual bounding box of the local spectral 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 constrained 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 the (u,v,n) coordinate system based on the limited support range, so as to convert the uniform sampling grid into a non-uniform sampling grid in the (x,y,z) coordinate system.

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

[0223]

[0224]

[0225] Where 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 represents the maximum and minimum wavenumber values, respectively; x0, y0, and z0 are the parameters in (x0, y0, z0), where (x0, y0, z0) represents the slowly changing stationary point; R represents the system's scan radius; z t,ζ,SA With z r,ζ,SAThese represent the transmit and receive antennas closest to z0 in the transmit and receive subarrays, respectively. t,min,SA z t,max,SA z r,min,SA z r,max,SA θ represents the minimum and maximum z-coordinates of the receiving subarray and the transmitting subarray, respectively; A1, A2 and B1, B2, B3, B4 are transitional parameters designed to facilitate the calculation process; ζ,SA θ represents the subarray scanning angle corresponding to the minimum difference in distance between the target point and the projection points of each antenna in the xoy plane; min,SA With θ max,SA θ represents the minimum and maximum scan angles of the subarray. mid,SA It is the midpoint of the subarray scanning angle.

[0226] Optionally, in one embodiment of this 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 downconversion processing on the sub-images of each subarray to obtain compressed sub-images.

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

[0229] A determination unit is used to determine the 3D imaging result based on a unique sub-image pair.

[0230] Optionally, in one embodiment of this application, the expression for frequency conversion processing in the space-time variable frequency drive 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 sub-image after SDC processing, f m,n (·) represents the reconstruction result of the m-th sub-image, j represents the complex exponential term; Φ is a spatially varying function of different locations (x, y, z) in space and the frequency of the transmitted signal, k max k min These represent the maximum and minimum wavenumber values, respectively. A1, A2, B1, B2, B3, and B4 are transitional parameters designed to facilitate the calculation process.

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

[0235] The near-field imaging device for a sparse MIMO array cylindrical scanning SAR system proposed in this application can divide the complete integrated array into multiple sub-arrays, reconstruct downsampled sub-images of each sub-array, and then obtain near-field three-dimensional imaging of the target linear sparse MIMO array cylindrical scanning SAR system by superimposing the sub-images. This achieves innovative derivation of analytical expressions for SDC and LLT by analyzing local spectral characteristics, using them for spectral compression of sub-images to reduce the number of sampling points, overcoming the high time and computational complexity of traditional time-domain imaging methods. Furthermore, the downsampled sub-images of each sub-array are reconstructed using time-domain imaging methods, and subsequent levels of sub-images are obtained by merging them pairwise, ultimately yielding accurate three-dimensional reconstruction results. This achieves accurate and efficient three-dimensional imaging of arbitrary linear sparse MIMO array cylindrical scanning SAR systems. Therefore, it solves the problems in related technologies, such as the need for improvement in imaging speed of traditional imaging methods and range migration methods based on non-uniform fast Fourier transform, and the limitations in imaging quality and computational efficiency that arise with increasing array sparsity.

[0236] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0237] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

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

[0239] Furthermore, electronic devices also include:

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

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

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

[0243] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, 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, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and 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 this application.

[0246] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system described above.

[0247] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the near-field imaging method of the sparse MIMO array cylindrical scanning SAR system provided in this application.

[0248] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

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

[0250] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0251] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing 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 (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs 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: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0252] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0253] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

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

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

Claims

1. A near-field imaging method for a sparse MIMO array cylindrical scanning SAR system, characterized in that, Includes the following steps: Complete echo data of the complete integrated array in a target linear sparse MIMO array cylindrical scanning SAR system is acquired, 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; Based on the local echo data, the sub-image corresponding to each sub-array is reconstructed in the non-uniform sampling grid corresponding to each sub-array in the plurality of sub-arrays; The sub-images of each sub-array are superimposed according to a preset superposition rule to obtain the three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system; The process of superimposing the sub-images of each sub-array according to a preset superposition rule to obtain the three-dimensional imaging result of the target linear sparse MIMO array cylindrical scan SAR system includes: The sub-images of each sub-array are subjected to spatial down-conversion processing to obtain compressed sub-images; The compressed sub-images are interpolated onto the adjacent higher-level sampling grids of each sub-array level to coherently superimpose the sub-images of each sub-array pairwise, resulting in multiple sub-images. This pairwise coherent superposition is repeated until a unique sub-image is obtained. The three-dimensional imaging result is determined based on the unique sub-image; The expression for the spatial frequency conversion process is: in, Indicates the number of SDC processes after... The reconstruction results of the sub-images, Indicates the first The reconstruction results of the sub-images, Indicates a complex exponential term; For different locations in space And a function of the frequency variation of the transmitted signal. , These represent the maximum and minimum wavenumber values, respectively. , and , , , These are all transitional parameters designed to facilitate the calculation process.

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

3. The near-field imaging method for a 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 of the plurality of sub-arrays, the method further includes: Based on the local spectral analysis results of the target linear sparse MIMO array cylindrical scanning SAR system, the spectral characteristics of the image of the target linear sparse MIMO array cylindrical scanning SAR system are simulated to analyze the local spectral support domain of the image at any location. The approximate minimum volume bounding box of the local spectral support domain is analyzed to transform the actual bounding box of the local spectral 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 constrained support range of the local spectrum. Based on the aforementioned limited support range, calculate the range of each subarray. A uniform sampling grid with a uniform sampling rate in the coordinate system is used to transform the uniform sampling grid into a coordinate system. Non-uniform sampling grid in the coordinate system.

4. The near-field imaging method for a sparse MIMO array cylindrical scanning SAR system according to claim 3, characterized in that, Each subarray in The formula for calculating a uniform sampling grid with a uniform sampling rate in a coordinate system is: in, , , They represent the converted values ​​respectively. coordinate; , These represent the maximum and minimum wavenumber values, respectively. , , They are respectively The various parameters in, Indicates a stationary point that changes slowly; Indicates the system's scan radius; and These represent the transmit and receive subarrays respectively. The latest transceiver antenna, , , , These represent the receiving subarrays respectively. Minimum and maximum coordinates, emitter subarray The minimum and maximum values ​​of the coordinates; , and , , , These are all transitional parameters designed to facilitate the calculation process; Indicates the distance between the target point and each antenna. The subarray scanning angle corresponding to the minimum difference in distance between planar projection points; and This represents the minimum and maximum scan angles of the subarray. It is the midpoint of the subarray scanning angle.

5. A near-field imaging device for a sparse MIMO array cylindrical scanning SAR system, characterized in that, include: The acquisition module is used to acquire the complete echo data of the 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 the local echo data corresponding to the multiple sub-arrays from the complete echo data; The reconstruction module is used to reconstruct 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 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 the three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system. The process of superimposing the sub-images of each sub-array according to a preset superposition rule to obtain the three-dimensional imaging result of the target linear sparse MIMO array cylindrical scan SAR system includes: The sub-images of each sub-array are subjected to spatial down-conversion processing to obtain compressed sub-images; The compressed sub-images are interpolated onto the adjacent higher-level sampling grids of each sub-array level to coherently superimpose the sub-images of each sub-array pairwise, resulting in multiple sub-images. This pairwise coherent superposition is repeated until a unique sub-image is obtained. The three-dimensional imaging result is determined based on the unique sub-image; The expression for the spatial frequency conversion process is: in, Indicates the number of SDC processes after... The reconstruction results of the sub-images, Indicates the first The reconstruction results of the sub-images, Indicates a complex exponential term; For different locations in space And a function of the frequency variation of the transmitted signal. , These represent the maximum and minimum wavenumber values, respectively. , and , , , These are all transitional parameters designed to facilitate the calculation process.

6. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the near-field imaging method for a sparse MIMO array cylindrical scanning SAR system as described in any one of claims 1-4.

8. 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 as described in any one of claims 1-4.

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