Handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression

Abstract

The invention relates to the technical field of millimeter wave near-field imaging, in particular to a handheld SAR (Synthetic Aperture Radar) three-dimensional fast near-field imaging method based on local spectrum compression, which comprises the following steps: acquiring original echo data, and dividing a complete comprehensive array into 2M-1 primary sub-arrays; calculating a uniform sampling grid with a uniform sampling rate of each primary sub-array in the (u, v, n) coordinate system, and converting the uniform sampling grid into a primary non-uniform sampling grid in the (x, y, z) coordinate system; reconstructing the (2M-1) first-level sub-arrays in the first-level non-uniform sampling grid to obtain (2M-1) first-level sub-images; and performing pairwise coherent superposition on the 2M-1 first-level sub-images to obtain at least two second-level sub-images, judging whether the second-level sub-images meet a preset condition or not, and if not, iteratively executing a pairwise coherent superposition process until the preset condition is met to obtain a three-dimensional reconstruction result. Therefore, the problems that a traditional imaging method is low in imaging quality, too high in calculation complexity and the like are solved.

Description

Technical Field

[0001] The invention relates to the technical field of millimeter wave near-field imaging, and in particular to a handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression and applicable to arbitrary trajectories. Background Art

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

[0003] In applications such as security inspection and nondestructive testing, 3D millimeter wave imaging systems are required to be not only efficient but also compact, portable and affordable. Handheld SAR imaging systems have become a competitive solution and have received extensive research attention due to their unique advantages, such as high flexibility, easy operation and high cost-effectiveness. Compared with traditional SAR systems, handheld systems use manual scanning of physical arrays. Although this method introduces uncertainty and non-uniformity in the scanning path, it also brings great convenience and adaptability in operation. This path uncertainty places higher requirements on image reconstruction methods, making the main challenges include accurate radar system motion tracking and the development of fast and accurate 3D imaging methods that can adapt to arbitrary scanning trajectories. Although accurate motion tracking of radar systems has been widely studied, there is still a lack of accurate and efficient 3D imaging methods for handheld SAR systems. Many application scenarios have extremely high requirements for imaging speed and quality, and accurate real-time imaging technology is urgently needed.

[0004] However, 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 the trajectory deviation of each scanning position, and can be directly applied to non-uniform integrated arrays. However, the actual implementation computational complexity is too high, and its application in real-time imaging systems is hindered. Most spatial wavenumber domain imaging methods are designed for uniform arrays. Although existing research literature has proposed methods suitable for non-uniform sparse arrays, such as the non-uniform fast Fourier transform-based range migration method (NUFFT-based RMA) and equivalent phase center (EPC) approximation, the imaging quality and computational efficiency are not high. Therefore, in response to the above problems, the present invention innovatively proposes a handheld fast decomposition back-projection three-dimensional imaging method, which can ensure both good imaging quality and high computational efficiency. Summary of the invention

[0005] The present invention provides a handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression, so as to solve the problem that the imaging accuracy and calculation efficiency cannot be guaranteed simultaneously in the imaging method of the close-range handheld SAR system.

[0006] The first aspect of the present invention provides a handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression, comprising the following steps: obtaining raw echo data emitted by a preset handheld SAR system; dividing the comprehensive aperture of the preset handheld SAR system according to the target level, and obtaining 2 M-1 The method comprises the steps of: calculating a uniform sampling grid with a uniform sampling rate for each first-level subarray in a (u, v, n) coordinate system by using LLT, and converting the uniform sampling grid into a first-level non-uniform sampling grid in a (x, y, z) coordinate system; and using the echo signal of each first-level subarray to sample the first-level non-uniform sampling grid for the 2 M-1 The first-level subarray is reconstructed to obtain 2 M-1 A first-level sub-image; M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result.

[0007] Optionally, the solution formula of the uniform sampling grid is:

[0008]

[0009] Among them, (u, v, n) is the coordinate system after LLT coordinate transformation, k max , k min are the lower and upper limits of the millimeter wave signal wave number, respectively, x′ min,SA , x′ max,SA , y′ min,SA and y′ max,SA are the upper and lower limits of x′ and y′ of each primary subarray, respectively. x, y, and z are the coordinates of the scattering points in the imaging area. 0,SA , y′ 0,SA are the x′ and y′ closest to x and y in each primary subarray, r is the sum of the distances between the scattering point coordinate p in the imaging area and the four vertices of the subarray, x′ min , x′ max , y′ min , y′ maxare the upper and lower limits of the x′ and y′ coordinates of the array element in the comprehensive aperture, respectively. t is the sum of the distances between the scattering point coordinate p in the imaging area and the four vertices of the subarray. The formula is:

[0010]

[0011] Optionally, the 2 M-1 The solution formula for a first-level sub-image is:

[0012]

[0013] Among them, f 1,n (p) is the sub-image of each primary sub-array, s(p′,τ) is the echo signal of each primary sub-array, τ is the two-way propagation delay between the array element and the scattering point in the imaging area, p is the coordinate of the scattering point in the imaging area, p′ is the coordinate of the array element in the comprehensive aperture, and c is the speed of light.

[0014] Optionally, the pair 2 M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, iteratively performing the pairwise coherent superposition process until the preset condition is met to obtain the sub-image of the target level, including:

[0015] Calculate the 2 M-2 The non-uniform sampling grid corresponding to the second-level sub-images;

[0016] The first-level sub-image of each sub-array is spatially down-converted using the SDC method to obtain 2 M-1 compressed sub-images;

[0017] The 2 M-1 The compressed sub-images are interpolated to their corresponding non-uniform sampling grids, and then each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain 2 M-1 The original spectrum sub-images;

[0018] The 2 M-1 The original spectrum sub-images are coherently superimposed on each other to obtain 2 M-2 sub-images;

[0019] Judge the 2 M-2 Whether the sub-images meet the preset conditions, if not, iteratively calculates new non-uniform sampling grids and performs pairwise coherent superposition until the preset conditions are met to obtain the three-dimensional reconstruction result.

[0020] Optionally, the solution formula for the compressed sub-image is:

[0021] f′ m,n(p) = f m,n (p)e -jΦ

[0022]

[0023] Among them, f′ m,n (p) is the compressed sub-image of the current level sub-image of the sub-array, m is the current level, 2≤m≤M, n=2 M-m is the number of subarrays at the current level, p is the coordinate of the scattering point in the imaging area, and f m,n (p) is the current level sub-image of the sub-array, e is the natural logarithm, j is the imaginary unit, Φ is the phase term, k max , k min are the lower and upper limits of the millimeter wave signal wave number, r is the sum of the distances between the spatial point p and the four vertices of the subarray, and x′ min , x′ max , y′ min , y′ max They are the x′ and y′ upper and lower limits of the array element coordinates within the synthetic aperture.

[0024] Optionally, the preset condition is M-2 Whether the sub-image is the final imaging result.

[0025] The second aspect of the present invention provides a handheld SAR three-dimensional fast near-field imaging device based on local spectrum compression, comprising: an acquisition module, used to acquire the original echo data emitted by a preset handheld SAR system; a division module, used to divide the comprehensive aperture of the preset handheld SAR system according to the target level, and obtain 2 M-1 a first-level subarray, and extracting the echo signal of each first-level subarray from the original echo data, wherein M is the target level, and M≥2; a network conversion module, for calculating a uniform sampling grid with a uniform sampling rate for each first-level subarray in the (u, v, n) coordinates by using LLT, and converting the uniform sampling grid into a first-level non-uniform sampling grid in the (x, y, z) coordinates; a reconstruction module, for reconstructing the 2 first-level subarrays by using the echo signal of each first-level subarray in the first-level non-uniform sampling grid; M-1 The first-level subarray is reconstructed to obtain 2 M-1 an iterative superposition module for iteratively superimposing the two M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result.

[0026] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression as described in the above embodiment.

[0027] A fourth aspect of the present invention provides a computer program product. When the computer program / instructions are executed by a processor, the method for three-dimensional fast near-field imaging of a handheld SAR based on local spectrum compression is implemented as described above.

[0028] A fifth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression.

[0029] The handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression proposed in an embodiment of the present invention divides the complete integrated array into multiple layers of sub-arrays, reconstructs the downsampled sub-image of each sub-array by using the time domain imaging method, and obtains the sub-images of the subsequent layers by merging them two by two, and finally obtains an accurate three-dimensional reconstruction result; at the same time, the analytical expressions of SDC and LLT are derived by analyzing the characteristics of the local spectrum, and are used to perform spectrum compression of the sub-image, thereby reducing the number of sampling points, overcoming the problem of high time complexity of traditional time domain imaging methods, and realizing accurate and efficient three-dimensional imaging of a handheld SAR system with arbitrary trajectory.

[0030] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0032] Figure 1 A schematic flow chart of a handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression provided by an embodiment of the present invention;

[0033] Figure 2 The geometric structure of the close-range handheld SAR imaging system provided by the embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a specific implementation of a handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression provided by an embodiment of the present invention;

[0035] Figure 4A schematic diagram of the local spectrum support domain of any three positions of a sub-image in an imaging area provided by an embodiment of the present invention;

[0036] Figure 5 Schematic diagram of comparison of imaging effect diagrams provided in an embodiment of the present invention, wherein (a) is the imaging result of the BPA imaging method, and (b) is the imaging result of an embodiment of the present invention;

[0037] Figure 6 A block diagram of a handheld SAR three-dimensional fast near-field imaging device based on local spectrum compression provided by an embodiment of the present invention;

[0038] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0040] The following describes a handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression according to an embodiment of the present invention with reference to the accompanying drawings. In view of the problem that imaging quality and computational efficiency cannot be taken into account in the traditional imaging method mentioned in the background technology center, the present invention provides a handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression. In this method, the complete integrated array is divided into multiple layers of sub-arrays, the sub-image spectrum is compressed using SDC and LLT, the down-sampled sub-image of each sub-array is reconstructed using a time domain imaging method, and the sub-images of subsequent levels are obtained by merging two by two, thereby achieving accurate and efficient three-dimensional imaging of a handheld SAR system with arbitrary trajectories, thereby solving the problem that imaging quality and computational efficiency cannot be taken into account in the traditional imaging method.

[0041] In order to facilitate those skilled in the art to understand the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression of the present application, the parameter symbols involved in the present application are explained in Table 1 below, and the geometric structure of the close-range handheld SAR imaging system is given to analyze the spectral characteristics of the SAR image, and the derivation process and results of LLT and SDC are given to provide theoretical support for the subsequent reduction of the computational load of 3D imaging.

[0042] Table 1

[0043]

[0044]

[0045] like Figure 1 As shown in the figure, the linear single-input single-output (SISO) array forms a synthetic aperture by manual scanning and illuminates the target in front of the aperture. Compared with the traditional SAR system using mechanical scanning, the handheld system shows great uncertainty in the scanning trajectory, resulting in fluctuations in the position of the array element in the synthetic aperture. The coordinates of the origin in the SISO array are expressed as p′=(x′,y′,z′), while the coordinates of the scattering points in the imaging area are expressed as p=(x,y,z).

[0046] The millimeter wave signal propagation process satisfies the first-order Born approximation and ignores the propagation attenuation. The echo signal can be expressed as:

[0047] s(p′,k)=∫∫∫f(p)e -j2k||p-p′|| dp (1)

[0048] Where s is the echo signal, k = 2πf / c, c is the speed of light, ||·|| is the amplitude of the vector, ||pp′|| is the distance between the array element and the scattering point, f(·) is the reflectivity distribution function of the target, which is also the objective function in the reconstruction process, e is the natural logarithm, and j is the imaginary unit.

[0049] Among the existing image reconstruction methods, BPA is widely regarded as the gold standard due to its excellent imaging quality. The measured signal is first transformed into the time domain using the fast inverse Fourier transform, and then reconstructed using BPA, the formula is:

[0050]

[0051] In the formula, k min , k max are the lower and upper limits of the millimeter wave signal wave number, respectively. The integration area of ​​formula (3) is the comprehensive aperture, and τ is the two-way propagation delay between the array element and the scattering point in the imaging area.

[0052] Substituting formula (2) into formula (3), the overall formula of BPA is:

[0053]

[0054] In formula (4), BPA reconstructs the imaging result pixel by pixel by performing spatial wavenumber integration at each position in the imaging area. Therefore, the computational complexity of BPA is proportional to the number of sampling points of the imaging result, and the number of sampling points is determined by the spectral bandwidth characteristics of the imaging result.

[0055] For close-range SAR images, the spectral characteristics including carrier frequency and bandwidth have strong spatial variability. Therefore, local spectrum analysis is required to better simulate the spectral characteristics of handheld SAR images.

[0056] The spatial wavenumber domain representation of the handheld SAR image can be directly calculated by 3D Fourier transform, that is,

[0057]

[0058] Where k = (k x ,k y ,k z ) is the spatial wavenumber domain variable of the image spectrum, k x ,k y and k z are the spatial wavenumber domain counterparts of x, y and z respectively.

[0059] Then, the stationary phase principle (POSP) is used to solve the above integrals over x, y, and z, and the formula is:

[0060]

[0061] Among them, α and p0 are the slowly varying amplitude term and the stationary point in POSP, respectively.

[0062] The stationing point meets:

[0063]

[0064] can be further simplified to:

[0065]

[0066] Therefore, any k=k0=(k x ,k y ,k z ) is:

[0067]

[0068] Alternatively, k0 can be expressed as a function of p′, k, and p0:

[0069]

[0070] Equations (9) and (10) determine the relationship between wavenumber domain variables and system parameter variables. x ,k y ,k z , the asymptotic integral result of formula (6) is non-zero only when (8) and (9) are satisfied, which is equivalent to a finite number of wave number domain points. This characteristic can be used to determine the spectral support range of the imaging result, and the formula is:

[0071]

[0072] Where D is the imaging area containing the imaging target, A is the comprehensive aperture, and for any position p0∈D in the imaging area, the corresponding spectrum support range can be calculated by formulas (8) and (9):

[0073]

[0074] It is defined as the local spectral support domain at p0. Therefore, the spectral support domain of the entire image can be expressed as the union of the local spectral support domains of each position in the imaging area, that is:

[0075]

[0076] Formulas (11)-(13) describe the characteristics of the spectrum support of handheld SAR images. Now the Nyquist sampling rate of each dimension can be derived accordingly, that is,

[0077]

[0078] Among them, k xmin , k xmax , k ymin , k ymax , k zmin and k zmax are k determined by formula (11) x , k y and k z The lower and upper limits of .

[0079] To simplify the formula, ignoring the small fluctuations of the array elements on the z-axis, the above formula can be further expanded to:

[0080]

[0081] Among them, x min 、x max ,y min ,y max and z min are the x,y lower and upper limits of the imaging, as well as the z lower limit, x′ min ,x′ max ,y′ min ,y′ max is the upper and lower limits of the comprehensive aperture x′, y′, x d,max and d,max is the maximum value of |xx′| and |yy′|, and the calculation formula is as follows:

[0082]

[0083] Finally, the number of sampling points required for the imaging result is:

[0084]

[0085] where V(D) is the volume of the imaging area D. From the above discussion, it can be seen that handheld SAR images can be modeled as spatially varying band-limited 3D signals, and the spectral support domain of the image at any position can be analyzed. This allows the design of efficient spectral compression methods combined with decomposition techniques, reducing sampling requirements and thus reducing the computational load of time domain reconstruction methods.

[0086] Furthermore, if Figure 2 As shown in the figure, the outline of the local spectrum support domain is a fan-shaped area, and its base is a curved quadrilateral when projected to the origin. In order to maximize the spectrum occupancy of the sub-image after the local spectrum transformation, the key points in the spatial wavenumber domain can be used to represent the approximate minimum volume bounding box of the local spectrum at any position p in the sub-array SA and the imaging area, which is defined as follows:

[0087] k1=k0((x′ min,SA ,y′ 0,SA ,0),p,k max )

[0088] k2=k0((x′ max,SA ,y′ 0,SA ,0),p,k max )

[0089] k3=k0((x′ 0,SA ,y′ min,SA ,0),p,k max )

[0090] k4=k0((x′ 0,SA ,y′ max,SA ,0),p,k max )

[0091] k5=(k0((x′ min,SA ,y′ min,SA ,0),p,k min 0+k0((x′ min,SA ,y′ max,SA ,0),p,k min )+k0((x′ max,sA ,y′ min,sA ,0),p,k min )+k0((x′ max,SA ,y′ max,SA ,0),p,k min )) / 4

[0092]

[0093] Among them, x′ min,SA, x′ max,SA , y′ min,SA and y′ max,SA are the upper and lower limits of x′ and y′ of the subarray SA, respectively. 0,SA , y′ 0,SA are x' and y' closest to x and y in the subarray SA, and the formula is:

[0094]

[0095]

[0096] The key points (k1, k2), (k3, k4) and (k5, k6) in the spatial wavenumber domain describe the span of the local spectral support domain along the azimuth and distance directions, respectively. Therefore, the approximate minimum volume bounding box of the local spectral support domain is represented by a parallelepiped consisting of three vectors k2-k1, k4-k3 and k6-k5. However, among these six key points, the curvature of the vector field formed by k6 is not zero with respect to p. Therefore, the integral factor method is used to convert k6 into an irrotational field by spatial variable scaling:

[0097] k7=α′(x,y,z)k6=α(x,y,z)k5 (20)

[0098]

[0099] Among them, α′(x,y,z) is the space-variant scaling factor, α(x,y,z)=2k max α′(x,y,z) / |k5|, the solution is:

[0100] α(x,y,z)=β(r)(22)

[0101] Where β(·) is any continuous differentiable function, r is the sum of the distances between the spatial point p and the four vertices of the subarray, and its formula is:

[0102]

[0103] Equations (20), (22), and (23) describe the general solution for converting k6 into an irrotational vector field by the integrating factor method, and then solving the arbitrary function β(·) as follows to obtain k7 that is identical to k6 in the axial direction of the subaperture.

[0104]

[0105] like Figure 2As shown in Figure 1, the positions of the key points k1, k2, k3, k4, k5 and k7 at different positions p. In addition, the parallelepiped formed by the vectors k2-k1, k4-k3 and k7-k5 is also represented by a wireframe. Based on this result, SDC and LLT are then used to transform the local spectrum bounding box into a unit box located at the origin of the spatial wavenumber domain to achieve a spectrum compression effect.

[0106] SDC is implemented by multiplying the sub-images in the spatial domain with a spatially variable phase term, assuming f m,n is the imaging result of any sub-array, then the operation of SDC is:

[0107] f′ m,n (p) = f m,n (p)e -jΦ (25)

[0108] The gradient of Φ should be the center k of the local spectral support region c ,Right now:

[0109]

[0110] Among them, k c Expressed as key points in the spatial wavenumber domain:

[0111]

[0112] The solution of Φ is:

[0113]

[0114] like Figure 2 As shown in Figure 1, after SDC, the local spectral support domain will be moved to the origin of the spatial wavenumber domain, thereby significantly reducing the sampling rate required for the sub-image.

[0115] Subsequently, LLT is used to transform the bounding box of the local spectrum support domain into a unit box at the origin of the spatial wavenumber domain to further improve the spectrum occupancy. The formulas for the three generating vectors of the parallelogram of the local spectrum support domain are:

[0116] v1=k2-k1; v2=k4-k3; v3=k7-k5 (29)

[0117] The linear transformations (including scaling, rotation, and shearing transformations) in the spatial wavenumber domain for the local spectral support domain are merged into the linear coordinate transformation matrix T k In the spatial wavenumber domain, the LLT operation can be expressed as:

[0118] 2πE=T k [v1v2v3] (30)

[0119] where E is the identity matrix. Therefore, after SDC and LLT, the spectrum of the sub-image is confined to a cubic box centered at the origin with a side length of 2π, which corresponds to a unit sampling rate in each dimension. k So the solution is:

[0120] T k =2π[v1v2v3] -1

[0121] According to the properties of Fourier transform, the required local linear coordinate transformation T in the spatial domain is derived s for:

[0122]

[0123] In order to implement LLT on the sub-images in the spatial domain, it is necessary to establish a coordinate transformation and locally linearize it to the desired locally linear transformation:

[0124]

[0125] According to formula (32), 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, and are solved according to formula (32):

[0126]

[0127]

[0128] The support range of the local spectrum after SDC and LLT is as follows Figure 3 As shown, it is the spectrum distribution within the unit box at the origin in the spatial wave number domain. After the above processing, SDC can be performed through formula (25) and sampling can be performed on the uniform sampling grid under the transformed coordinates (u, v, n) (formula (33)), and the sub-image of each sub-array can be reconstructed with the least sampling points.

[0129] Based on the above theoretical analysis, Figure 3 As shown, the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression proposed in an embodiment of the present invention specifically includes the following steps:

[0130] In step S301, original echo data emitted by a preset handheld SAR system is obtained.

[0131] Specifically, the amplitude and delay inconsistency between different channel signals of the preset handheld SAR system is corrected, and the amplitude imbalance between different frequencies of each channel signal of the preset handheld SAR system is corrected to obtain the original echo data s(p′,k).

[0132] In step S302, the comprehensive aperture of the preset handheld SAR system is divided according to the target level to obtain 2 M -1 The first-level sub-arrays are divided into two groups, and the echo signal of each first-level sub-array is extracted from the original echo data, where M is the target level and M≥2.

[0133] Specifically, the complete comprehensive aperture of the preset handheld SAR system is divided into two according to the target level M: M-1 The corresponding subarray SA is extracted from the original echo data. 1,n echo data.

[0134] Taking the target level m=3 as an example, the original echo data is divided into four primary sub-arrays, namely SA 1,1 ,SA 1,2 ,SA 1,3 and SA 1,4 , and extract the corresponding subarray SA from the original echo data 1,1 ,SA 1,2 ,SA 1,3 and SA 1,4 echo data.

[0135] In step S303, LLT is used to calculate a uniform sampling grid with a uniform sampling rate for each primary subarray in the (u, v, n) coordinate system, and the uniform sampling grid is converted into a primary non-uniform sampling grid in the (x, y, z) coordinate system.

[0136] Specifically, LLT is used to calculate the uniform sampling grid with uniform sampling rate for each primary subarray in the (u, v, n) coordinate system, and then formula (33) is used to convert the uniform sampling grid in the (u, v, n) coordinate system to a non-uniform sampling grid in the (x, y, z) coordinate system, and the first-level non-uniform sampling grid G ​​of each primary subarray is obtained. 1,n , taking the target level m = 3 as an example, the first-level non-uniform sampling grids of each first-level subarray are G 1,1 , G 1,2 , G 1,3 and G 1,4 .

[0137] In step S304, the echo signal of each primary subarray is used to sample the first-level non-uniform grid pair 2. M-1 The first-level subarray is reconstructed to obtain 2M-1 A first-level sub-image.

[0138] Specifically, Figure 4 As shown, the BPA imaging method is used to sample the non-uniform sampling grid G ​​at each primary subarray. 1,n Reconstruct each sub-array sub-image f 1,n (p), the reconstruction formula is:

[0139]

[0140] The integral region of formula (34) corresponds to the subarray SA 1,n , s(p′,τ) is the echo signal of each primary subarray, τ is the two-way propagation delay between the array element and the scattering point in the imaging area, p is the coordinate of the scattering point in the imaging area, p′ is the coordinate of the array element in the comprehensive aperture, and c is the speed of light.

[0141] From formulas (14)-(15), we can see that as the aperture decreases, the resolution of the sub-image decreases. This decrease makes the sub-image f of each sub-array 1,n (p) In each primary subarray, the non-uniform sampling grid G 1,n The sampling rate is reduced, thus reducing the overall computational burden of BPA.

[0142] Taking the target level M=3 as an example, based on the BPA imaging method, using the subarray SA 1,1 The echo data is sampled in a non-uniform grid G 1,1 In the example, the SA of the subarray 1,1 The echo data is reconstructed to obtain the first-level sub-image f 1,1 (p), using subarray SA 1,2 The echo data is sampled in a non-uniform grid G 1,2 In the example, the SA of the subarray 1,2 The echo data is reconstructed to obtain the first-level sub-image f 1,2 (p), using subarray SA 1,3 The echo data is sampled in a non-uniform grid G 1,3 In the example, the SA of the subarray 1,3 The echo data is reconstructed to obtain the first-level sub-image f 1,3 (p), using subarray SA 1,4 The echo data is sampled in a non-uniform grid G 1,4 In the example, the SA of the subarray 1,4 The echo data is reconstructed to obtain the first-level sub-image f 1,4 (p).

[0143] In step S305, M-1The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result.

[0144] In some embodiments, M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result, including:

[0145] Calculation 2 M-2 The non-uniform sampling grid corresponding to the second-level sub-images;

[0146] The SDC method is used to perform spatial down-conversion on the first-level sub-image of each sub-array, and 2 M-1 compressed sub-images;

[0147] 2 M-1 The compressed sub-images are interpolated to their corresponding non-uniform sampling grids, and then each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain 2 M-1 The original spectrum sub-images;

[0148] 2 M-1 The original spectrum sub-images are coherently superimposed on each other to obtain 2 M-2 sub-images;

[0149] Judgment 2 M-2 The sub-images meet the preset conditions. If not, a new non-uniform sampling grid is iteratively calculated and two-by-two coherent superposition is performed until the preset conditions are met to obtain a 3D reconstruction result. The preset conditions are 2 M-2 Whether the sub-image is the final imaging result.

[0150] In some embodiments, the solution formula for compressing the sub-image is:

[0151] f′ m,n (p) = f m,n (p)e -jΦ

[0152]

[0153] Among them, f′ m,n (p) is the compressed sub-image of the current level sub-image of the sub-array, m is the current level, 2≤m≤M, n=2 M-M is the number of subarrays at the current level, p is the coordinate of the scattering point in the imaging area, and f m,n(p) is the current level sub-image of the sub-array, e is the natural logarithm, j is the imaginary unit, Φ is the phase term, k max , k min are the lower and upper limits of the millimeter wave signal wave number, r is the sum of the distances between the spatial point p and the four vertices of the subarray, and x′ min , x′ max , y′ min , y′ max They are the x′ and y′ upper and lower limits of the array element coordinates within the synthetic aperture.

[0154] Specifically, Figure 4 As shown, first calculate 2 M-2 Secondary sub-image f 2,n The corresponding non-uniform sampling grid G 2,n , using the SDC method to transform the first-level sub-image f of each sub-array 1,n Perform spatial down-conversion operation to obtain 2 M-1 compressed sub-images, including sub-image f 1,2n-1 and f 1,2n ; 2 M-1 The compressed sub-images are interpolated to their corresponding non-uniform sampling grid G 2,n Then, each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain 2 M-1 The original spectrum sub-images are converted into 2 M-1 The original spectrum sub-images are coherently superimposed on each other to form 2 M-2 sub-images, and obtain the secondary sub-image f 2,n .

[0155] Similarly, the imaging result of the mth level is obtained by coherently superposing the sub-images of the m-1th level by two pairs, which is achieved by interpolation. To avoid aliasing during the interpolation process, the sub-image of the m-1th level is first spatially down-converted using formulas (25) and (28) to convert the sub-image into a compressed form f′ m,2n-1 and f′ m,2n ; interpolate these m-1th level sub-images with well-defined spectral support ranges to the mth level sampling grid G m,n Then, the sub-image is multiplied by its conjugate space down-conversion function to restore the spectral position and ensure the coherence of the sub-image superposition process:

[0156] f m,n (p) = f′ m,n (p)e jΦ (35)

[0157] Then, the processed m-1th level sub-images are coherently superimposed using formula (36) to obtain m,n The upsampled m-th sub-image.

[0158] f m,n (p) = f m-1,2n-1 (p)+f m-1,2n (p),p∈G m,n (36)

[0159] This operation is repeated to finally obtain the Mth level imaging result, which is the 3D imaging result of the handheld SAR system.

[0160] Taking the target level M = 3 as an example, the first-level sub-image f is calculated respectively 1,1 and f 1,2 The sub-image pair composed of f 1,3 、f 1,4 The non-uniform sampling grid G ​​corresponding to the sub-image pair 2,1 , G 2,2 , using the SDC method, each first-level sub-image f 1,1 、f 1,2 、f 1,3 、f 1,4 Perform spatial down-conversion operation to obtain each compressed sub-image f′ 1,1 , f′ 1,2 , f′ 1,3 , f′ 1,4 ; Each compressed sub-image f′ 1,1 , f′ 1,2 , f′ 1,3 , f′ 1,4 Interpolate to its corresponding non-uniform sampling grid G 2,1 , G 2,2 Then, each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain the corresponding original spectrum sub-image f 1,1 、f 1,2 、f 1,3 、f 1,4 , and then perform pairwise coherent superposition to form two sub-images f 2,1 and f 2,2 ;

[0161] Furthermore, since the two sub-images f 2,1 and f 2,2 If it is not the final imaging result, then calculate the secondary sub-image f 2,1 and f 2,2 The non-uniform sampling grid G ​​corresponding to the sub-image pair 3,1 , using the SDC method to transform each secondary sub-image f 2,1 and f 2,2 Perform spatial down-conversion operation to obtain each compressed sub-image f′ 2,1 , f′ 2,2 ; Each compressed sub-image f′ 2,1 , f′2,2 Interpolate to its corresponding non-uniform sampling grid G 3,1 Then, each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain the corresponding original spectrum sub-image f 2,1 and f 2,2 , and then perform pairwise coherent superposition to obtain the sub-image f 3,1 , since the sub-image f 3,1 is the final imaging result, then the sub-image f 3,1 The three-dimensional reconstruction result is output.

[0162] Taking the target level M = 4 as an example, the first-level sub-image f is calculated respectively 1,1 、f 1,2 The sub-image pair composed of 1,3 、f 1,4 The sub-image pair composed of 1,5 、f 1,6 The sub-image pair composed of 1,7 、f 1,8 The non-uniform sampling grid G ​​corresponding to the sub-image pair 2,1 , G 2,2 , G 2,3 , G 2,4 , using the SDC method, each first-level sub-image f 1,1 、f 1,2 、f 1,3 、f 1,4 、f 1,5 、f 1,5 、f 1,6 、f 1,7 、f 1,8 Perform spatial down-conversion operation to obtain each compressed sub-image f′ 1,1 , f′ 1,2 , f′ 1,3 , f′ 1,4 , f′ 1,5 , f′ 1,6 , f′ 1,7 , f′ 1,8 ;

[0163] Each compressed sub-image f′ 1,1 , f′ 1,2 , f′ 1,3 , f′ 1,4 , f′ 1,5 , f′ 1,6 , f′ 1,7 , f′ 1,8 Interpolate to its corresponding non-uniform sampling grid G 2,1 , G 2,2 , G 2,3 , G 2,4Then, each compressed sub-image is multiplied by its spatial down-conversion function to obtain the corresponding original spectrum sub-image f 1,1 、f 1,2 、f 1,3 、f 1,4 、f 1,5 、f 1,6 、f 1,7 、f 1,8 , and then perform pairwise coherent superposition to form 4 sub-images f 2,1 、f 2,2 、f 2,3 、f 2,4 ;

[0164] Furthermore, since the four sub-images f 2,1 、f 2,2 、f 2,3 、f 2,4 If it is not the final imaging result, the secondary sub-images f are calculated separately. 2,1 、f 2,2 The sub-image pair composed of f 2,3 、f 2,4 The non-uniform sampling grid G ​​corresponding to the sub-image pair 3,1 , G 3,2 , using the SDC method to transform each secondary sub-image f 2,1 、f 2,2 、f 2,3 、f 2,4 Perform spatial down-conversion operation to obtain each compressed sub-image f′ 2,1 , f′ 2,2 , f′ 2,3 , f′ 2,4 ; Each compressed sub-image f′ 2,1 , f′ 2,2 , f′ 2,3 , f′ 2,4 Interpolate to its corresponding non-uniform sampling grid G 3,1 , G 3,2 Then, each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain the corresponding original spectrum sub-image f 2,1 、f 2,2 、f 2,3 、f 2,4 , and then perform pairwise coherent superposition to form two sub-images f 3,1 and f 3,2 ;

[0165] Furthermore, since the two sub-images f 3,1 and f 3,2 If it is not the final imaging result, the three-level sub-image f is calculated. 3,1 and f 3,2The non-uniform sampling grid G ​​corresponding to the sub-image pair 4,1 , using the SDC method to transform each three-level sub-image f 3,1 and f 3,2 Perform spatial down-conversion operation to obtain each compressed sub-image f′ 3,1 , f′ 3,2 ; Each compressed sub-image f′ 3,1 , f′ 3,2 Interpolate to its corresponding non-uniform sampling grid G 4,1 Then, each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain the corresponding original spectrum sub-image f 3,1 、f 3,2 , and then coherently superimpose them to obtain the sub-image f 4,1 , since the sub-image f 4,1 is the final imaging result, then the sub-image sub-image f 4,1 The three-dimensional reconstruction result is output, and the process is the same as when M=3.

[0166] Therefore, if Figure 5 As shown, in order to solve the problem of high computational complexity of the traditional BPA time-domain imaging method, the embodiment of the present application performs effective spectrum compression on the spatial down-conversion (SDC) and local linear transformation (LLT) of the handheld SAR system, reconstructs the down-sampled sub-image of each sub-array by the time-domain imaging method, and obtains the sub-images of the subsequent levels by merging two by two, thereby realizing accurate and efficient near-field three-dimensional imaging of the handheld SAR system with arbitrary trajectory. For the traditional BPA time-domain imaging method, the computational complexity of the BPA method used as the benchmark is O(N 5 ) level, while the embodiment of the present application only needs O(N 3 logN) computational complexity, achieving accurate and efficient near-field three-dimensional imaging of a handheld SAR system with arbitrary trajectory.

[0167] In summary, the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression proposed in an embodiment of the present invention has the following beneficial effects:

[0168] (1) In view of the characteristics of the spectrum support range of close-range handheld SAR images, the embodiments of the present invention analyze the shape of the wavenumber domain spectrum at different positions in space, and analytically express the space-varying characteristics of the spectrum of close-range handheld SAR images, thereby providing theoretical support for subsequently reducing the number of sampling points in the imaging interval and thus reducing the computational burden;

[0169] (2) To address the problem that the spatial down-conversion (SDC) and local linear transform (LLT) operations implemented in spectrum compression require complex numerical pre-calculations, the embodiments of the present invention derive SDC and LLT analytical expressions applicable to a handheld SAR imaging system whose array structure changes at any time, thereby avoiding the large amount of time required for numerical pre-calculations and applying them to spectrum compression of sub-images;

[0170] (3) The embodiments of the present invention solve the problem of image quality degradation caused by approximate processing in existing handheld SAR imaging methods, and can achieve an imaging quality that is almost the same as that of the benchmark back projection method (BPA);

[0171] (4) The embodiment of the present invention solves the problem of high computational complexity of the traditional time domain imaging method, and only requires O(N) 3 logN) computational complexity, accurate and efficient near-field three-dimensional imaging of a handheld SAR system with any trajectory can be achieved.

[0172] Next, a handheld SAR three-dimensional fast near-field imaging device based on local spectrum compression proposed in an embodiment of the present invention will be described with reference to the accompanying drawings.

[0173] Figure 6 It is a block diagram of a handheld SAR three-dimensional fast near-field imaging device based on local spectrum compression according to an embodiment of the present invention.

[0174] like Figure 6 As shown, the handheld SAR three-dimensional fast near-field imaging device 60 based on local spectrum compression includes: an acquisition module 601, a division module 602, a network conversion module 603, a reconstruction module 604 and an iterative superposition module 605.

[0175] The acquisition module 601 is used to acquire the original echo data of the preset handheld SAR transmitting system. The division module 602 is used to divide the comprehensive aperture of the preset handheld SAR system according to the target level to obtain 2 M-1 The first-level subarrays are extracted from the original echo data, where M is the target level and M≥2. The network conversion module 603 is used to calculate the uniform sampling grid with uniform sampling rate for each first-level subarray in the (u, v, n) coordinate system by using LLT, and convert the uniform sampling grid into a first-level non-uniform sampling grid in the (x, y, z) coordinate system. The reconstruction module 604 is used to use the echo signal of each first-level subarray to reconstruct the first-level non-uniform sampling grid. M-1 The first-level subarray is reconstructed to obtain 2 M-1 The iterative superposition module 605 is used to iteratively superimpose 2 M-1The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result.

[0176] In some embodiments, the solution formula for the uniform sampling grid is:

[0177]

[0178] Among them, (u, v, n) is the coordinate system after LLT coordinate transformation, k max , k min are the lower and upper limits of the millimeter wave signal wave number, respectively, x′ min,SA , x′ max,SA , y′ min,SA and y′ max,SA are the upper and lower limits of x′ and y′ of each primary subarray, respectively. x, y, and z are the coordinates of the scattering points in the imaging area. 0,SA , y′ 0,SA are the x′ and y′ closest to x and y in each primary subarray, r is the sum of the distances between the scattering point coordinate p in the imaging area and the four vertices of the subarray, x′ min , x′ max , y′ min , y′ max They are the x′ and y′ upper and lower limits of the array element coordinates within the synthetic aperture.

[0179] In some embodiments, 2 M-1 The solution formula for a first-level sub-image is:

[0180]

[0181] Among them, f 1,n (p) is the sub-image of each primary sub-array, s(p′,τ) is the echo signal of each primary sub-array, τ is the two-way propagation delay between the array element and the scattering point in the imaging area, p is the coordinate of the scattering point in the imaging area, p′ is the coordinate of the array element in the comprehensive aperture, and c is the speed of light.

[0182] In some embodiments, the iterative superposition module includes:

[0183] 2 M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result, including:

[0184] Calculation 2 M-2 The non-uniform sampling grid corresponding to the second-level sub-images;

[0185] The SDC method is used to perform spatial down-conversion on the first-level sub-image of each sub-array, and 2 M-1 compressed sub-images;

[0186] 2 M-1 The compressed sub-images are interpolated to their corresponding non-uniform sampling grids, and then each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain 2 M-1 The original spectrum sub-images;

[0187] 2 M-1 The original spectrum sub-images are coherently superimposed on each other to obtain 2 M-2 sub-images;

[0188] Judgment 2 M-2 Whether the sub-image meets the preset conditions, if not, iterate and calculate 2 M-2 The non-uniform sampling grids corresponding to the sub-images are selected and pairwise coherent superposition is performed until the preset conditions are met to obtain the 3D reconstruction result.

[0189] In some embodiments, the solution formula for compressing the sub-image is:

[0190] f′ m,n (p) = f m,n (p)e -jΦ

[0191]

[0192] Among them, f′ m,n (p) is the compressed sub-image of the current level sub-image of the sub-array, m is the current level, 2≤m≤M, n=2 M-m is the number of subarrays at the current level, p is the coordinate of the scattering point in the imaging area, and f m,n (p) is the current level sub-image of the sub-array, e is the natural logarithm, j is the imaginary unit, Φ is the phase term, k max , k min are the lower and upper limits of the millimeter wave signal wave number, r is the sum of the distances between the spatial point p and the four vertices of the subarray, and x′ min , x′ max , y′ min , y′ max They are the x′ and y′ upper and lower limits of the array element coordinates within the synthetic aperture.

[0193] In some embodiments, the preset condition is 2 M-2 Whether the sub-image is the final imaging result.

[0194] It should be noted that the aforementioned explanation of the embodiment of the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression is also applicable to the handheld SAR three-dimensional fast near-field imaging device based on local spectrum compression in this embodiment, and will not be repeated here.

[0195] The handheld SAR three-dimensional fast near-field imaging device based on local spectrum compression proposed in an embodiment of the present invention has the following beneficial effects:

[0196] (1) In view of the characteristics of the spectrum support range of close-range handheld SAR images, the embodiments of the present invention analyze the shape of the wavenumber domain spectrum at different positions in space, and analytically express the space-varying characteristics of the spectrum of close-range handheld SAR images, thereby providing theoretical support for subsequently reducing the number of sampling points in the imaging interval and thus reducing the computational burden;

[0197] (2) To address the problem that the spatial down-conversion (SDC) and local linear transform (LLT) operations implemented in spectrum compression require complex numerical pre-calculations, the embodiments of the present invention derive SDC and LLT analytical expressions applicable to a handheld SAR imaging system whose array structure changes at any time, thereby avoiding the large amount of time required for numerical pre-calculations and applying them to spectrum compression of sub-images;

[0198] (3) The embodiments of the present invention solve the problem of image quality degradation caused by approximate processing in existing handheld SAR imaging methods, and can achieve an imaging quality that is almost the same as that of the benchmark back projection method (BPA);

[0199] (4) The embodiment of the present invention solves the problem of high computational complexity of the traditional time domain imaging method, and only requires O(N) 3 logN) computational complexity, accurate and efficient near-field three-dimensional imaging of a handheld SAR system with any trajectory can be achieved.

[0200] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0201] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0202] When the processor 702 executes the program, the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression provided in the above embodiment is implemented.

[0203] Furthermore, the electronic device further comprises:

[0204] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0205] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0206] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0207] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0208] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0209] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0210] An embodiment of the present invention further provides a computer program product. When the computer program / instructions are executed by a processor, the computer program / instructions implement the above-mentioned handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression.

[0211] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression.

[0212] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0213] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0214] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0215] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0216] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0217] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0218] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0219] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression, characterized in that: The following steps are involved: Acquire the raw echo data emitted by the preset handheld SAR system; The comprehensive aperture of the preset handheld SAR system is divided according to the target level, and 2 M-1 first-level subarrays, and extracting the echo signal of each first-level subarray from the original echo data, wherein M is the target level, and M≥2; Using LLT to calculate a uniform sampling grid with a uniform sampling rate for each primary subarray in the (u, v, n) coordinate system, and converting the uniform sampling grid into a primary non-uniform sampling grid in the (x, y, z) coordinate system; Using the echo signal of each primary subarray to sample the two M-1 The first-level subarray is reconstructed to obtain 2 M-1 First-level sub-images; Regarding the 2 M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result.

2. The handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression according to claim 1 is characterized in that: The solution formula for the uniform sampling grid is: Among them, (u, v, n) is the coordinate system after LLT coordinate transformation, k max , k min are the lower and upper limits of the millimeter wave signal wave number, respectively, x ′ min,SA 、x ′ max,SA ,y ′ min,SA and ′ max,SA are x of each first-level subarray ′ , y′ upper and lower limits, x, y, z are the coordinates of the scattering points in the imaging area, x ′ 0,SA ,y ′ 0,SA are the x′ and y′ closest to x and y in each primary subarray, r is the sum of the distances between the scattering point coordinate p in the imaging area and the four vertices of the subarray, x ′ min 、x ′ max ,y ′ min ,y ′ max are the x coordinates of the array element in the synthetic aperture ′ , upper and lower limits of y′.

3. The handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression according to claim 1 is characterized in that: 2 M-1 The solution formula for a first-level sub-image is: Among them, f 1,n (p) is the sub-image of each first-level sub-array, s(p ′ ,τ) is the echo signal of each primary subarray, τ is the two-way propagation delay between the array element and the scattering point in the imaging area, p is the coordinate of the scattering point in the imaging area, and p ′ is the coordinate of the array element within the comprehensive aperture, and c is the speed of light.

4. The handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression according to claim 1 is characterized in that: The two M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result, including: Calculation 2 M-2 The non-uniform sampling grid corresponding to the second-level sub-images; The first-level sub-image of each sub-array is spatially down-converted using the SDC method to obtain 2 M-1 compressed sub-images; The 2 M-1 The compressed sub-images are interpolated to their corresponding non-uniform sampling grids, and then each compressed sub-image is multiplied by its conjugate space down-conversion function to obtain 2 M-1 The original spectrum sub-images; The 2 M-1 The original spectrum sub-images are coherently superimposed on each other to obtain 2 M-2 sub-images; Judge the 2 M-2 Whether the sub-images meet the preset conditions, if not, iteratively calculates new non-uniform sampling grids and performs pairwise coherent superposition until the preset conditions are met to obtain the three-dimensional reconstruction result.

5. The handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression according to claim 4 is characterized in that: The solution formula for the compressed sub-image is: f m ′ ,n (p)=f m,n (p)e -jΦ Among them, f m ′ ,n (p) is the compressed sub-image of the current level sub-image of the sub-array, m is the current level, 2≤m≤M, n=2 M-m is the number of subarrays at the current level, p is the coordinate of the scattering point in the imaging area, and f m,n (p) is the current level sub-image of the sub-array, e is the natural logarithm, j is the imaginary unit, Φ is the phase term, k max , k min are the lower and upper limits of the millimeter wave signal wave number, r is the sum of the distances between the spatial point p and the four vertices of the subarray, x ′ min 、x ′ max ,y ′ min ,y ′ max are the x coordinates of the array element in the synthetic aperture ′ , upper and lower limits of y′.

6. The handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression according to claim 4 is characterized in that: The preset condition is the 2 M-2 Whether the sub-image is the final imaging result.

7. A handheld SAR three-dimensional fast near-field imaging device based on local spectrum compression, characterized in that: include: An acquisition module, used for acquiring raw echo data emitted by a preset handheld SAR system; A division module is used to divide the comprehensive aperture of the preset handheld SAR system according to the target level to obtain 2 M -1 first-level subarrays, and extracting the echo signal of each first-level subarray from the original echo data, wherein M is the target level, and M≥2; A network conversion module, for calculating a uniform sampling grid with a uniform sampling rate for each primary subarray in a (u, v, n) coordinate system using LLT, and converting the uniform sampling grid into a primary non-uniform sampling grid in a (x, y, z) coordinate system; A reconstruction module is used to use the echo signal of each primary sub-array to reconstruct the 2 M-1 The first-level subarray is reconstructed to obtain 2 M-1 First-level sub-images; Iterative superposition module, used to iteratively superimpose the two M-1 The first-level sub-images are coherently superimposed in pairs to obtain at least two second-level sub-images, and it is determined whether the at least two second-level sub-images meet a preset condition. If not, the pairwise coherent superposition process is iteratively performed until the preset condition is met to obtain a three-dimensional reconstruction result.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression as described in any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program / instruction is executed by a processor, the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the handheld SAR three-dimensional fast near-field imaging method based on local spectrum compression as described in any one of claims 1 to 6.

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