Planar aperture sparse non-uniform sampling three-dimensional imaging method, device and storage medium

Abstract

The present disclosure relates to a planar aperture sparse non-uniform sampling three-dimensional imaging method, device and storage medium, the method comprising: sparse non-uniform sampling an observation area in the range direction, the azimuth direction and the height direction; distance compression is carried out on echo data to obtain the position of the target area; the target area is divided into several units in the range direction, the azimuth direction and the height direction, and a distance matrix is constructed; an observation equation is constructed based on the distance matrix; and a three-dimensional complex image of the target is obtained through the observation equation. Through the embodiments of the present disclosure, the problem of high sidelobes caused by sparse non-uniform sampling is effectively solved, and the resolution and data acquisition efficiency are improved.

Description

Technical Field

[0001] This disclosure relates to the field of sparse non-uniform sampling technology, specifically to a planar aperture sparse non-uniform sampling three-dimensional imaging method, a planar aperture sparse non-uniform sampling three-dimensional imaging device, and a computer-readable storage medium. Background Technology

[0002] In existing technologies, when performing 3D imaging processing on sparsely and non-uniformly sampled echo data, the imaging results obtained using Fourier transform-based imaging algorithms often exhibit high sidelobes and grating lobes, resulting in poor image quality. Furthermore, randomly sampling fully sampled data before processing the sparse data does not consider directly performing sparse and non-uniform sampling on the observed target, thus failing to reduce hardware costs. Summary of the Invention

[0003] This disclosure aims to provide a planar aperture sparse non-uniform sampling three-dimensional imaging method, a planar aperture sparse non-uniform sampling three-dimensional imaging device, and a computer-readable storage medium, which effectively solves the high sidelobe problem caused by sparse non-uniform sampling, improves resolution, and improves data acquisition efficiency.

[0004] According to one of the solutions disclosed herein, a planar aperture sparse non-uniform sampling three-dimensional imaging method is provided, comprising:

[0005] Sparse and non-uniform sampling is performed on the observation area in the range, azimuth, and altitude directions;

[0006] Distance compression is performed on the echo data to obtain the location of the target area;

[0007] The target area is divided into several units in the range, azimuth, and altitude directions, and a range matrix is ​​constructed.

[0008] Construct observation equations based on the distance matrix;

[0009] A three-dimensional complex image of the target is obtained through the observation equation.

[0010] In some embodiments, the sparse non-uniform sampling of the observation area in the range, azimuth, and altitude directions includes:

[0011] The echo signal obtained when the observation area is not sparsely and non-uniformly sampled in the three directions of range, azimuth, and altitude;

[0012] In the azimuth direction, the azimuth sampling criterion is used to perform sparse and non-uniform sampling of the observation area; in the range direction, the range sampling criterion is used to perform sparse and non-uniform sampling of the observation area; and in the height direction, the height sampling criterion is used to perform sparse and non-uniform sampling of the observation area.

[0013] The echo data is obtained as a three-dimensional complex matrix.

[0014] In some embodiments, the step of dividing the target area into several units in the range, azimuth, and altitude directions to construct a range matrix includes:

[0015] The target area is divided into grids in the distance direction with a step size, resulting in a total of several distance cells. The target area is also divided into grids in the azimuth direction with a step size, resulting in a total of several azimuth cells. The target area is further divided into grids in the height direction with a step size, resulting in a total of several height cells.

[0016] Construct the distance matrix.

[0017] In some embodiments, the construction of the observation equation based on the distance matrix includes:

[0018] The observation matrix is ​​obtained by processing the distance matrix;

[0019] Construct a target region raster matrix based on the observation matrix;

[0020] Construct the echo matrix based on the raster matrix of the target region;

[0021] Construct the observation equation.

[0022] In some embodiments, obtaining a three-dimensional complex image of the target through an observation equation includes:

[0023] The established observation equations are solved using the orthogonal matching pursuit algorithm;

[0024] Calculate the column vector of scattering information for the observation area;

[0025] The scattering information column vectors are rearranged into a scattering information matrix to obtain a three-dimensional complex image of the target.

[0026] According to one of the solutions disclosed herein, a planar aperture sparse non-uniform sampling three-dimensional imaging device is provided, comprising:

[0027] The sampling module is configured to perform sparse and non-uniform sampling of the observation area in the range, azimuth, and height directions.

[0028] The compression module is configured to perform distance compression on the echo data to obtain the location of the target area;

[0029] The matrix construction module is configured to divide the target area into several units in the range, azimuth, and height directions to construct a range matrix.

[0030] The equation construction module is configured to construct observation equations based on the distance matrix.

[0031] The image module is configured to obtain a three-dimensional complex image of the target through observation equations.

[0032] In some embodiments, the sampling module is further configured to:

[0033] The echo signal obtained when the observation area is not sparsely and non-uniformly sampled in the three directions of range, azimuth, and altitude;

[0034] In the azimuth direction, the azimuth sampling criterion is used to perform sparse and non-uniform sampling of the observation area; in the range direction, the range sampling criterion is used to perform sparse and non-uniform sampling of the observation area; and in the height direction, the height sampling criterion is used to perform sparse and non-uniform sampling of the observation area.

[0035] The echo data is obtained as a three-dimensional complex matrix.

[0036] In some embodiments, the equation construction module is further configured to:

[0037] The observation matrix is ​​obtained by processing the distance matrix;

[0038] Construct a target region raster matrix based on the observation matrix;

[0039] Construct the echo matrix based on the raster matrix of the target region;

[0040] Construct the observation equation.

[0041] In some embodiments, the image module is further configured to:

[0042] The established observation equations are solved using the orthogonal matching pursuit algorithm;

[0043] Calculate the column vector of scattering information for the observation area;

[0044] The scattering information column vectors are rearranged into a scattering information matrix to obtain a three-dimensional complex image of the target.

[0045] According to one of the solutions of this disclosure, a computer-readable storage medium is provided, having stored thereon computer-executable instructions, which, when executed by a processor, implement:

[0046] Based on the above-mentioned planar aperture sparse non-uniform sampling three-dimensional imaging method.

[0047] The planar aperture sparse non-uniform sampling three-dimensional imaging method, device, and computer-readable storage medium of various embodiments disclosed herein perform sparse non-uniform sampling of the observation area in at least the range, azimuth, and height directions; compress the echo data to obtain the location of the target area; divide the target area into several units in the range, azimuth, and height directions to construct a range matrix; construct an observation equation based on the range matrix; and obtain a three-dimensional complex image of the target through the observation equation. This method aims to fully consider the influence of sparse non-uniform sampling data, as directly processing it using conventional imaging methods would result in high sidelobes. The method proposed in this invention can perform sparse non-uniform sampling of the observation area in three directions (range, azimuth, and height), which can reduce hardware costs, improve data processing speed, and apply compressed sensing (CS) reconstruction technology to the three-dimensional imaging system, effectively solving the high sidelobes problem caused by sparse non-uniform sampling, improving resolution, and increasing data acquisition efficiency.

[0048] It should be understood that the foregoing general description and the following detailed description are exemplary and illustrative only, and are not intended to limit the scope of this disclosure. Attached Figure Description

[0049] In drawings that are not necessarily drawn to scale, similar reference numerals in different views may indicate similar components. Similar reference numerals with letter suffixes or similar reference numerals with different letter suffixes may indicate different instances of similar components. The drawings are generally used to illustrate various embodiments by way of example rather than limitation, and are used together with the specification and claims to explain the disclosed embodiments.

[0050] Figure 1 A flowchart of a planar aperture sparse non-uniform sampling three-dimensional imaging method according to an embodiment of the present disclosure is shown;

[0051] Figure 2 A planar aperture three-dimensional imaging geometric model of an embodiment of the present disclosure is shown;

[0052] Figure 3 A schematic diagram of a planar aperture sparse non-uniform sampling three-dimensional imaging apparatus according to an embodiment of the present disclosure is shown. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0054] Synthetic Aperture Radar (SAR) is an emerging imaging technology whose main advantage is its ability to penetrate certain obstacles, making it highly valuable in disaster monitoring, marine surveillance, military reconnaissance, and radar target imaging. Throughout the development of SAR technology, much research has focused on acquiring high-resolution target images. However, traditional SAR methods are costly and generate massive amounts of echo data.

[0055] Typically, uniformly spaced, densely sampled data in the target observation space is acquired using tomographic SAR or holographic SAR, and then high-resolution target images can be obtained through certain imaging algorithms. However, this uniformly spaced sampling method is time-consuming and costly. To solve this problem, sparse non-uniform sampling 3D imaging technology has emerged, which can obtain high-resolution target images while ensuring low cost and small echo data volume.

[0056] In recent years, with the advent of compressed sensing (CS) theory, signal acquisition methods have undergone a revolutionary change. This theory demonstrates that accurate reconstruction of sparse, non-uniform data or signals can be achieved from a limited number of observations using methods such as basis pursuit. In recent years, CS theory has received widespread attention in many SAR-related application areas and has achieved certain results.

[0057] For sparse non-uniform sampling 3D imaging technology, existing technologies have a certain research foundation. For example, there is a non-uniform array imaging system based on CS (Simulation Coding) technology for imaging processing; another example is a sparse array layout method and frequency domain sparse imaging processing method proposed based on Barker code sparse sampling. However, this method mainly involves randomly sampling fully sampled data and then performing imaging processing on the sparse data. It does not consider directly performing sparse non-uniform sampling on the observed target, nor does it reduce hardware costs.

[0058] In conjunction with the preceding background section, this disclosure provides illustrative examples of solutions to address the deficiencies in the prior art, but these are not intended to limit the scope of patent protection claimed in this disclosure.

[0059] As one of the solutions, such as Figure 1As shown, embodiments of this disclosure provide a planar aperture sparse non-uniform sampling three-dimensional imaging method, including:

[0060] Sparse and non-uniform sampling is performed on the observation area in the range, azimuth, and altitude directions;

[0061] Distance compression is performed on the echo data to obtain the location of the target area;

[0062] The target area is divided into several units in the range, azimuth, and altitude directions, and a range matrix is ​​constructed.

[0063] Construct observation equations based on the distance matrix;

[0064] A three-dimensional complex image of the target is obtained through the observation equation.

[0065] To address the problems mentioned above, the embodiments of this disclosure aim to propose a planar aperture sparse non-uniform sampling three-dimensional imaging method. In each embodiment, sparse non-uniform sampling of the observation area is performed in three directions: range, azimuth, and height. This fully considers the impact of sparse non-uniform sampling data, as directly processing it using conventional imaging methods would result in high sidelobes. Furthermore, it reduces hardware costs and improves data processing speed. During the imaging process, by performing sparse non-uniform sampling of the observation area in the range, azimuth, and height directions, and further applying compressed sensing (CS) reconstruction technology to the three-dimensional imaging system, the high sidelobes caused by sparse non-uniform sampling can be effectively solved, improving resolution and data acquisition efficiency.

[0066] For example, the specific imaging process can be implemented as a technical solution represented by the following steps.

[0067] In some implementations, the method of this disclosure may include: performing sparse non-uniform sampling of the observation area in the range, azimuth, and altitude directions, comprising:

[0068] The echo signal obtained when the observation area is not sparsely and non-uniformly sampled in the three directions of range, azimuth, and altitude;

[0069] In the azimuth direction, the azimuth sampling criterion is used to perform sparse and non-uniform sampling of the observation area; in the range direction, the range sampling criterion is used to perform sparse and non-uniform sampling of the observation area; and in the height direction, the height sampling criterion is used to perform sparse and non-uniform sampling of the observation area.

[0070] The echo data is obtained as a three-dimensional complex matrix.

[0071] For example, the method in this embodiment may include:

[0072] Step S1: Perform sparse non-uniform sampling of the observation area in the three directions of range, azimuth, and altitude; the specific sparse non-uniform sampling can be implemented as including but not limited to steps S11 to S13.

[0073] Step S11: Set up a row of sparse array antennas in the azimuth direction. Each antenna element transmits a stepped-frequency continuous wave signal in the range direction. A three-dimensional resolution capability for the observation area is achieved through mechanical movement in the altitude direction. Establish a spatial rectangular coordinate system with the center of the observation scene as the origin, the azimuth direction as the x-axis, the range direction as the y-axis, and the altitude direction as the z-axis. The distance between the center of the planar aperture and the center of the observation scene is R. Any target point P in the observation scene... n The coordinates of the point are (xn, yn, zn), and the coordinates of any sampling point P in the planar aperture are (xn, yn, zn). a ,R,z a The target scattering coefficient is σn, and the geometric model for planar aperture three-dimensional imaging is as follows: Figure 2 As shown;

[0074] Step S12: Observe the area in the three directions of range, azimuth, and altitude. (N represents the total number of grid cells in the observation area) When sparse and non-uniform sampling is not performed, the obtained echo signal s(x) a ,R,z a ):

[0075]

[0076] Among them, the scattered echo signal s(x) a ,R,z a ) is N a ×N r ×N h A three-dimensional complex matrix, N a N represents the number of sampling points in the azimuth direction. h N represents the number of height-based sampling points. r x is the number of sampling points in the distance direction. a The z-coordinate represents the coordinate of the sampling point on the X' axis. a k represents the coordinates of the sampling point on the Z' axis. ω The magnitude of the wavenumber vector corresponding to the stepped-frequency continuous wave signal, kω, is related to the instantaneous frequency f and the speed of light c by kω. ω =2πf / c, k ω ∈[k ωmin ,k ωmax ], k ωmin and k ωmax Let P and Z represent the magnitudes of the wavenumber vectors corresponding to the lowest and highest frequencies, respectively. The distance between the sampling point P and the geometric center of the target Pn(xn, yn, zn) is:

[0077]

[0078] Step S13: Use in azimuth direction The sampling criterion employs sparse and non-uniform sampling of the observation area, using the range direction... The sampling criteria employ sparse and non-uniform sampling of the observation area, using [a specific method] in the height direction. The sampling criterion performs sparse and non-uniform sampling of the observation area, where Δx, Δf, and Δz are the sampling intervals in the azimuth, range, and height directions, respectively, satisfying the Nyquist sampling criterion. The sparsity ξ is shown in equation (3).

[0079]

[0080] At this point, the number of sparse, non-uniform sampling points obtained in the range, azimuth, and altitude directions are M respectively. f =n f M x =n x M z =n z The echo data S obtained by sparse non-uniform sampling is M x ×M f ×M z The three-dimensional complex matrix enables sparse and non-uniform sampling of the observation area, which greatly reduces the amount of echo data and thus reduces hardware costs.

[0081] In some implementations, the method of this disclosure may include: dividing the target area into several units in the range, azimuth, and altitude directions, and constructing a range matrix, comprising:

[0082] The target area is divided into grids in the distance direction with a step size, resulting in a total of several distance cells. The target area is also divided into grids in the azimuth direction with a step size, resulting in a total of several azimuth cells. The target area is further divided into grids in the height direction with a step size, resulting in a total of several height cells.

[0083] Construct the distance matrix.

[0084] For example, the method of this embodiment may further include the following steps based on step S1:

[0085] Step S2: Perform range compression on the sparsely and non-uniformly sampled echo data to obtain the location C' of the target region.

[0086]

[0087] Among them, (x' n ,y' n ,z' n) represents the location of any target in the target area, and N' represents the total number of imaging grids in the target area.

[0088] In some implementations, the method of this disclosure may include: constructing the observation equation based on the distance matrix, comprising:

[0089] The observation matrix is ​​obtained by processing the distance matrix;

[0090] Construct a target region raster matrix based on the observation matrix;

[0091] Construct the echo matrix based on the raster matrix of the target region;

[0092] Construct the observation equation.

[0093] For example, the method of this embodiment may further include the following based on step S2:

[0094] Step S3: For the target area obtained in step S2, in the distance direction [Y] a ,Y b With step size Y c The grid is divided into N grids. y Each distance unit corresponds to the target area's azimuth direction [X]. a ,X b [With step size X] c The grid is divided into N grids. x Each azimuth unit, with height orientation [Z] of the target area. a Z b With step size Z c The grid is divided into N grids. z Construct a distance matrix for each height unit. Where N' = N x N y N z R' is the distance matrix from the sampling point to the target region, as shown in equation (5):

[0095]

[0096] in, Indicates the height towards the Mth z Secondary sparse non-uniform sampling, azimuth direction Mth x The second sparse non-uniform sampling, distance to the Mth... f The distance from the sampling point to the nth target grid is the result of subsparse non-uniform sampling.

[0097] Step S4: Process the distance matrix R' constructed in step S3 to obtain the observation matrix. As shown in equation (6):

[0098] Among them, Mx M represents the number of sparse, non-uniform sampling points in the azimuth direction. f M represents the number of sparse, non-uniform sampling points along the distance. z Let N' be the number of highly sparse, non-uniform sampling points. x N y N z This represents the total number of all grid cells in the target area;

[0099]

[0100] Step S5: Construct the target region raster matrix As shown in equation (7):

[0101]

[0102] in, Indicates the orientation of the target area towards the Nth direction. x The nth grid, distance to the Nth grid y The nth grid cell, with height increasing towards the Nth cell. z The scattering coefficients of the target in each grid cell are rearranged σ in column vector form as follows: As shown in equation (8):

[0103]

[0104] Step S6: Construct the echo matrix As shown in equation (9)

[0105]

[0106] in Indicates the direction to the Mth x The second sparse non-uniform sampling, distance to the Mth... f Secondary sparse non-uniform sampling, height-oriented Mth... z The echo values ​​from the sub-sparse, non-uniform sampling are rearranged into column vector form as follows:

[0107] As shown in equation (10):

[0108]

[0109] Step S7: Construct the observation equation based on steps S3 to S6 as shown in equation (11):

[0110] Φσ'=S' (11)

[0111] in, For the observation matrix, This is the target scattering coefficient matrix.

[0112] In some implementations, the method of this disclosure may include: obtaining a three-dimensional complex image of the target through an observation equation, including:

[0113] The established observation equations are solved using the orthogonal matching pursuit algorithm;

[0114] Calculate the column vector of scattering information for the observation area;

[0115] The scattering information column vectors are rearranged into a scattering information matrix to obtain a three-dimensional complex image of the target.

[0116] For example, the method of this embodiment may further include the following additions based on steps S3 to S6:

[0117] Step S7: Three-dimensional imaging of sparse and non-uniformly sampled data with planar aperture is equivalent to solving the sparse reconstruction problem shown in equation (12). The orthogonal matching pursuit algorithm based on the l1 norm optimization method is used to solve the established observation equation:

[0118]

[0119] Calculate the scattering information column vector σ' of the observation area according to equation (12), and then rearrange σ' into the form of the scattering information matrix σ, thus obtaining the three-dimensional complex image of the target. This is the reconstructed 3D complex image.

[0120] Some embodiments of this disclosure are applied to imaging processing of sparse non-uniform sampled data. The orthogonal matching pursuit algorithm is used when imaging processing sparse non-uniform data, but it is not limited to this algorithm. Other reconstruction algorithms under the compressed sensing framework can also be applied.

[0121] As one of the solutions, such as Figure 3 As shown, embodiments of this disclosure provide a planar aperture sparse non-uniform sampling three-dimensional imaging device, comprising:

[0122] The sampling module is configured to perform sparse and non-uniform sampling of the observation area in the range, azimuth, and height directions.

[0123] The compression module is configured to perform distance compression on the echo data to obtain the location of the target area;

[0124] The matrix construction module is configured to divide the target area into several units in the range, azimuth, and height directions to construct a range matrix.

[0125] The equation construction module is configured to construct observation equations based on the distance matrix.

[0126] The image module is configured to obtain a three-dimensional complex image of the target through observation equations.

[0127] As one implementation, the planar aperture sparse non-uniform sampling three-dimensional imaging device of this disclosure can be further configured, in conjunction with the steps described above, as follows:

[0128] The echo signal obtained when the observation area is not sparsely and non-uniformly sampled in the three directions of range, azimuth, and altitude;

[0129] In the azimuth direction, the azimuth sampling criterion is used to perform sparse and non-uniform sampling of the observation area; in the range direction, the range sampling criterion is used to perform sparse and non-uniform sampling of the observation area; and in the height direction, the height sampling criterion is used to perform sparse and non-uniform sampling of the observation area.

[0130] The echo data is obtained as a three-dimensional complex matrix.

[0131] As one implementation, the planar aperture sparse non-uniform sampling three-dimensional imaging device of this disclosure can be further configured, in conjunction with the steps described above, of the matrix construction module as follows:

[0132] The target area is divided into grids in the distance direction with a step size, resulting in a total of several distance cells. The target area is also divided into grids in the azimuth direction with a step size, resulting in a total of several azimuth cells. The target area is further divided into grids in the height direction with a step size, resulting in a total of several height cells.

[0133] Construct the distance matrix.

[0134] As one implementation, the planar aperture sparse non-uniform sampling three-dimensional imaging device of this disclosure can be further configured, in conjunction with the steps described above, that the equation construction module is:

[0135] The observation matrix is ​​obtained by processing the distance matrix;

[0136] Construct a target region raster matrix based on the observation matrix;

[0137] Construct the echo matrix based on the raster matrix of the target region;

[0138] Construct the observation equation.

[0139] As one implementation, the planar aperture sparse non-uniform sampling three-dimensional imaging device of this disclosure can be further configured, in conjunction with the steps described above, as follows:

[0140] The established observation equations are solved using the orthogonal matching pursuit algorithm;

[0141] Calculate the column vector of scattering information for the observation area;

[0142] The scattering information column vectors are rearranged into a scattering information matrix to obtain a three-dimensional complex image of the target.

[0143] For example, the modules and sub-modules of the planar aperture sparse non-uniform sampling three-dimensional imaging device of this embodiment can be further configured to implement the following steps:

[0144] Step S1: Perform sparse, non-uniform sampling of the observation area in the three directions of range, azimuth, and altitude; specifically,

[0145] Step S11: Set up a row of sparse array antennas in the azimuth direction. Each antenna element transmits a stepped-frequency continuous wave signal in the range direction. A three-dimensional resolution capability for the observation area is achieved through mechanical movement in the altitude direction. Establish a spatial rectangular coordinate system with the center of the observation scene as the origin, the azimuth direction as the x-axis, the range direction as the y-axis, and the altitude direction as the z-axis. The distance between the center of the planar aperture and the center of the observation scene is R. Any target point P in the observation scene... n The coordinates of the point are (xn, yn, zn), and the coordinates of any sampling point P in the planar aperture are (xn, yn, zn). a ,R,z a The target scattering coefficient is σn, and the geometric model for planar aperture three-dimensional imaging is as follows: Figure 2 As shown;

[0146] Step S12: Observe the area in the three directions of range, azimuth, and altitude. (N represents the total number of grid cells in the observation area) When sparse and non-uniform sampling is not performed, the obtained echo signal s(x) a ,R,z a ):

[0147]

[0148] Among them, the scattered echo signal s(x) a ,R,z a ) is N a ×N r ×N h A three-dimensional complex matrix, N a N represents the number of sampling points in the azimuth direction. h N represents the number of height-based sampling points. r x is the number of sampling points in the distance direction. a The z-coordinate represents the coordinate of the sampling point on the X' axis. a k represents the coordinates of the sampling point on the Z' axis. ω The magnitude of the wavenumber vector corresponding to the stepped-frequency continuous wave signal, kω, is related to the instantaneous frequency f and the speed of light c by kω. ω =2πf / c, k ω ∈[kωmin ,k ωmax ], k ωmin and k ωmax Let P and Z represent the magnitudes of the wavenumber vectors corresponding to the lowest and highest frequencies, respectively. The distance between the sampling point P and the geometric center of the target Pn(xn, yn, zn) is:

[0149]

[0150] Step S13: Use in azimuth direction The sampling criterion employs sparse and non-uniform sampling of the observation area, using the range direction... The sampling criteria employ sparse and non-uniform sampling of the observation area, using [a specific method] in the height direction. The sampling criterion performs sparse and non-uniform sampling of the observation area, where Δx, Δf, and Δz are the sampling intervals in the azimuth, range, and height directions, respectively, satisfying the Nyquist sampling criterion. The sparsity ξ is shown in equation (3).

[0151]

[0152] At this point, the number of sparse, non-uniform sampling points obtained in the range, azimuth, and altitude directions are M respectively. f =n f M x =n x M z =n z The echo data S obtained by sparse non-uniform sampling is M x ×M f ×M z The three-dimensional complex matrix enables sparse and non-uniform sampling of the observation area, which greatly reduces the amount of echo data and thus reduces hardware costs.

[0153] Step S2: Perform range compression on the sparsely and non-uniformly sampled echo data to obtain the location C' of the target region.

[0154]

[0155] Among them, (x' n ,y' n ,z' n ) represents the position of any target in the target area, and N' represents the total number of imaging grids in the target area;

[0156] Step S3: For the target area obtained in step S2, in the distance direction [Y] a ,Y b With step size Y c The grid is divided into N grids. y Each distance unit corresponds to the target area's azimuth direction [X].a ,X b [With step size X] c The grid is divided into N grids. x Each azimuth unit, with height orientation [Z] of the target area. a Z b With step size Z c The grid is divided into N grids. z Construct a distance matrix for each height unit. Where N' = N x N y N z R' is the distance matrix from the sampling point to the target region, as shown in equation (5):

[0157]

[0158] in, Indicates the height towards the Mth z Secondary sparse non-uniform sampling, azimuth direction Mth x The second sparse non-uniform sampling, distance to the Mth... f The distance from the sampling point to the nth target grid is the result of subsparse non-uniform sampling.

[0159] Step S4: Process the distance matrix R' constructed in step S3 to obtain the observation matrix. As shown in equation (6):

[0160]

[0161] Among them, M x M represents the number of sparse, non-uniform sampling points in the azimuth direction. f M represents the number of sparse, non-uniform sampling points along the distance. z Let N' be the number of highly sparse, non-uniform sampling points. x N y N z This represents the total number of all grid cells in the target area;

[0162] Step S5: Construct the target region raster matrix As shown in equation (7):

[0163]

[0164] in, Indicates the orientation of the target area towards the Nth direction. x The nth grid, distance to the Nth grid y The nth grid cell, with height increasing towards the Nth cell. z The scattering coefficients of the target in each grid cell are rearranged σ in column vector form as follows: As shown in equation (8):

[0165]

[0166] Step S6: Construct the echo matrix As shown in equation (9)

[0167]

[0168] in Indicates the direction to the Mth x The second sparse non-uniform sampling, distance to the Mth... f Secondary sparse non-uniform sampling, height-oriented Mth... z The echo values ​​from the sub-sparse, non-uniform sampling are rearranged into column vector form as follows: As shown in equation (10):

[0169]

[0170] Step S7: Construct the observation equation based on steps S3-S6 as shown in equation (11):

[0171] Φσ'=S' (11)

[0172] in, For the observation matrix, The target scattering coefficient matrix;

[0173] Step S7: Three-dimensional imaging of sparse and non-uniformly sampled data with planar aperture is equivalent to solving the sparse reconstruction problem shown in equation (12). The orthogonal matching pursuit algorithm based on the l1 norm optimization method is used to solve the established observation equation:

[0174]

[0175] Calculate the scattering information column vector σ' of the observation area according to equation (12), and then rearrange σ' into the form of the scattering information matrix σ, thus obtaining the three-dimensional complex image of the target.

[0176] This is the reconstructed 3D complex image.

[0177] Specifically, one of the inventive concepts disclosed herein aims to utilize the aforementioned planar aperture sparse non-uniform sampling three-dimensional imaging method, planar aperture sparse non-uniform sampling three-dimensional imaging device, and computer-readable storage medium. This method primarily involves sparse non-uniform sampling of the observation area in at least the range, azimuth, and height directions; range compression of the echo data to obtain the target area's location; division of the target area into several units in the range, azimuth, and height directions to construct a range matrix; construction of an observation equation based on the range matrix; and obtaining a three-dimensional complex image of the target through the observation equation. This approach fully considers the impact of sparse non-uniform sampling data, as directly processing it using conventional imaging methods would result in high sidelobes. The method proposed in this invention can perform sparse non-uniform sampling of the observation area in the range, azimuth, and height directions, reducing hardware costs, increasing data processing speed, and applying compressed sensing (CS) reconstruction technology to the three-dimensional imaging system. This effectively solves the high sidelobes problem caused by sparse non-uniform sampling, improving resolution and data acquisition efficiency.

[0178] The beneficial effects of the embodiments disclosed herein are at least reflected in:

[0179] 1) Sparse and non-uniform sampling of the observation area in the three directions of range, azimuth, and altitude can reduce hardware costs and improve data processing speed;

[0180] 2) Applying compressed sensing (CS) reconstruction technology to 3D imaging systems can effectively solve the problem of high sidelobes caused by sparse and non-uniform sampling, improve resolution and data acquisition efficiency;

[0181] 3) In the case of large data volume and long running time during imaging processing, the various embodiments disclosed herein can effectively reduce the impact of high sidelobes and grating lobes on image quality, reduce hardware costs, and improve data processing efficiency.

[0182] This disclosure also provides a computer-readable storage medium storing computer-executable instructions thereon, which, when executed by a processor, mainly implement the above-described planar aperture sparse non-uniform sampling three-dimensional imaging method, including at least:

[0183] Sparse and non-uniform sampling is performed on the observation area in the range, azimuth, and altitude directions;

[0184] Distance compression is performed on the echo data to obtain the location of the target area;

[0185] The target area is divided into several units in the range, azimuth, and altitude directions, and a range matrix is ​​constructed.

[0186] Construct observation equations based on the distance matrix;

[0187] A three-dimensional complex image of the target is obtained through the observation equation.

[0188] The above embodiments are merely exemplary embodiments of this disclosure and are not intended to limit this disclosure. The scope of protection of this disclosure is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this disclosure within its substance and scope, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this disclosure.

Claims

1. A planar aperture sparse non-uniform sampling three-dimensional imaging method, including: Sparse and non-uniform sampling is performed on the observation area in the range, azimuth, and altitude directions; Distance compression is performed on the echo data to obtain the location of the target area; The target area is divided into several units in the range, azimuth, and altitude directions, and a range matrix is ​​constructed. Construct observation equations based on the distance matrix; A three-dimensional complex image of the target is obtained through the observation equation; in: The observation equations are constructed based on the distance matrix using compressed sensing technology, including: The observation matrix is ​​obtained by processing the distance matrix. ; Constructing a target region raster matrix based on the observation matrix : ， in, Indicates the orientation of the target area towards the first The distance to the first grid cell, from the... The grid, with height towards the first... The scattering coefficient of the target in each grid cell will Rearranged in column vector form as follows : ； The echo matrix is ​​constructed based on the raster matrix of the target region, including: Constructing the echo matrix : ， in, Indicates the direction to the first The second sparse non-uniform sampling, the distance to the first The second sparse non-uniform sampling, height-oriented... The echo values ​​of the sparse and non-uniformly sampled data are used to obtain the echo data. Rearranged in column vector form as follows ; Construct the observation equation: .

2. The method according to claim 1, wherein, The sparse, non-uniform sampling of the observation area in the range, azimuth, and altitude directions includes: The echo signal obtained when the observation area is not sparsely and non-uniformly sampled in the three directions of range, azimuth, and altitude; In the azimuth direction, the azimuth sampling criterion is used to perform sparse and non-uniform sampling of the observation area; in the range direction, the range sampling criterion is used to perform sparse and non-uniform sampling of the observation area; and in the height direction, the height sampling criterion is used to perform sparse and non-uniform sampling of the observation area. The echo data is obtained as a three-dimensional complex matrix.

3. The method according to claim 2, wherein, The process of dividing the target area into several units along the range, azimuth, and altitude directions, and constructing a range matrix, includes: The target area is divided into grids in the distance direction with a step size, resulting in a total of several distance cells. The target area is also divided into grids in the azimuth direction with a step size, resulting in a total of several azimuth cells. The target area is further divided into grids in the height direction with a step size, resulting in a total of several height cells. Construct the distance matrix.

4. The method according to claim 3, wherein, The observation equation yields a three-dimensional complex image of the target, including: The established observation equations are solved using the orthogonal matching pursuit algorithm; Calculate the column vector of scattering information for the observation area; The scattering information column vectors are rearranged into a scattering information matrix to obtain a three-dimensional complex image of the target.

5. A planar aperture sparse non-uniform sampling three-dimensional imaging device, comprising: The sampling module is configured to perform sparse and non-uniform sampling of the observation area in the range, azimuth, and height directions. The compression module is configured to perform distance compression on the echo data to obtain the location of the target area; The matrix construction module is configured to divide the target area into several units in the range, azimuth, and height directions to construct a range matrix. The equation construction module is configured to construct observation equations based on the distance matrix. The image module is configured to obtain a three-dimensional complex image of the target through observation equations; in: The observation equations are constructed based on the distance matrix using compressed sensing technology, including: The observation matrix is ​​obtained by processing the distance matrix. ; Constructing a target region raster matrix based on the observation matrix : ， in, Indicates the orientation of the target area towards the first The distance to the first grid cell, from the... The grid, with height towards the first... The scattering coefficient of the target in each grid cell will Rearranged in column vector form as follows : ； The echo matrix is ​​constructed based on the raster matrix of the target region, including: Construct the echo matrix: , ， in, Indicates the direction to the first The second sparse non-uniform sampling, the distance to the first The second sparse non-uniform sampling, height-oriented... The echo values ​​of the sparse and non-uniformly sampled data are used to obtain the echo data. Rearranged in column vector form as follows ; Construct the observation equation: .

6. The apparatus according to claim 5, wherein, The sampling module is further configured as follows: The echo signal obtained when the observation area is not sparsely and non-uniformly sampled in the three directions of range, azimuth, and altitude; In the azimuth direction, the azimuth sampling criterion is used to perform sparse and non-uniform sampling of the observation area; in the range direction, the range sampling criterion is used to perform sparse and non-uniform sampling of the observation area; and in the height direction, the height sampling criterion is used to perform sparse and non-uniform sampling of the observation area. The echo data is obtained as a three-dimensional complex matrix.

7. The apparatus according to claim 6, wherein, The image module is further configured as follows: The established observation equations are solved using the orthogonal matching pursuit algorithm; Calculate the column vector of scattering information for the observation area; The scattering information column vectors are rearranged into a scattering information matrix to obtain a three-dimensional complex image of the target.

8. A computer-readable storage medium having stored thereon computer-executable instructions, which, when executed by a processor, implement: The planar aperture sparse non-uniform sampling three-dimensional imaging method according to any one of claims 1 to 4.

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