Impact system ISAR imaging method based on compressed sensing theory

Abstract

The invention relates to an impact system ISAR imaging method based on a compressed sensing theory, and relates to the field of radar imaging. Performing polynomial fitting on the echo trace points through an echo matrix observed in the azimuth direction to obtain a track curve; solving a geometrical relationship to obtain a rotation angle corresponding to each echo; performing envelope alignment on the original echo data, and constructing a sparse measurement matrix and a sparse model; thirdly, target echo data of sparse observation are divided into a plurality of column vectors according to distance units, and reconstruction data are obtained; and finally, carrying out operations such as interpolation resampling and the like to obtain an ISAR image of the impact system. The method is based on a compressed sensing theory, is different from traditional rotary table model ISAR imaging, and constructs sparse measurement matrixes of different latitudes according to echo and track characteristics, thereby restoring and reconstructing complete echo signals and obtaining high-quality imaging results.

Description

Technical Field

[0001] The present invention relates to the field of radar imaging, and in particular to an impact system ISAR imaging method based on compressed sensing theory. Background Art

[0002] With the development of modern electronic technology, the focus of radar development has shifted to multi-application technology systems that improve performance in range resolution, target imaging, and target recognition. Ultra-wideband radar technology provides this special capability, with advantages that conventional radars cannot match, such as high range resolution, low probability of interception, anti-stealth, anti-interference, anti-anti-radiation missiles, and strong penetration. Therefore, the study of impact radar imaging has great application value and strategic value.

[0003] In traditional ISAR imaging of chirp signals or stepped frequency signals, high-resolution images rely on the coherent processing between radar intermediate frequency, bandwidth and backscattered signals of non-cooperative target corners. Generally, the range image resolution is obtained through Fourier transform, and the azimuth resolution is obtained through azimuth calibration. Ultra-wideband microwave radar can achieve ultra-wideband through linear frequency modulation or discontinuous steps of carrier frequency, and can also use pseudo-random phase encoding to achieve carrier-free ultra-wideband radar, also known as video radar, non-sinusoidal wave radar, etc. A radar that emits an ultra-short impulse pulse is a carrier-free impulse system ultra-wideband radar.

[0004] At present, due to the extremely narrow pulse and carrier-free form, impact radar generally adopts time domain imaging algorithm, which is mostly used for SAR imaging, such as ground penetrating radar. Impact radar based on SAR imaging does not have the problem of mapping grid transformation, and generally images cooperative targets. Some scholars use a close-range turntable model to perform impact ISAR imaging on metal spheres. The purpose is also to obtain an accurate mapping grid, and the turntable model can achieve full angle coverage measurement, and there will be no situation where information is missing and the imaging results are incomplete. However, for impact ISAR imaging of long-range non-cooperative targets, the lack of angle information and full angle coverage will inevitably make it impossible to obtain all the information of the target. More new methods need to be studied to achieve impact ISAR imaging. Summary of the invention

[0005] The technical problems to be solved by the present invention are:

[0006] In order to avoid the shortcomings of the prior art, the present invention provides an impact system ISAR imaging method based on compressed sensing theory, which is used to solve the problem that the prior art cannot obtain perfect long-range non-cooperative target impact system ISAR imaging results.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] An impact system ISAR imaging method based on compressed sensing theory, characterized by comprising:

[0009] Receive the echo data of the impact system radar, determine the radar system parameters, obtain the target track information, and form the original echo matrix;

[0010] Envelope aligning and centering the original echo matrix to form a first echo matrix, and obtaining a center point distance;

[0011] Constructing a uniformly varying first turning angle data grid;

[0012] According to the first turning angle data grid, interpolating the first echo matrix to obtain a second echo matrix;

[0013] Construct a sparse observation matrix based on angle sparsity and a second rotation angle data grid;

[0014] Reconstructing the second echo matrix according to the sparse measurement matrix to obtain a third echo matrix;

[0015] According to the second turning angle data grid, obtaining a coordinate mapping grid corresponding to each point of the second turning angle data grid;

[0016] Obtaining a projection image of the third echo matrix based on the coordinate mapping grid;

[0017] The impact system ISAR imaging result is obtained based on the projection image.

[0018] An impact system ISAR imaging method based on compressed sensing theory, characterized by comprising:

[0019] Receive the echo data of the impact system radar, determine the radar system parameters, obtain the target track information, and form the original echo matrix;

[0020] Envelope aligning and centering the original echo matrix to form a transition echo matrix, and obtaining a center point distance;

[0021] Constructing a sparse measurement matrix based on aperture sparsity according to the sparse aperture;

[0022] Reconstructing the transition echo matrix according to the aperture-sparse based sparse measurement matrix to obtain a first echo matrix;

[0023] Constructing a uniformly varying first turning angle data grid;

[0024] According to the first turning angle data grid, interpolating the first echo matrix to obtain a second echo matrix;

[0025] Construct a sparse observation matrix based on angle sparsity and a second rotation angle data grid;

[0026] Reconstructing the second echo matrix according to the angle-sparse based sparse measurement matrix to obtain a third echo matrix;

[0027] According to the second turning angle data grid, obtaining a coordinate mapping grid corresponding to each point of the second turning angle data grid;

[0028] Obtaining a projection image of the third echo matrix based on the coordinate mapping grid;

[0029] The impact system ISAR imaging result is obtained based on the projection image.

[0030] A further technical solution of the present invention is as follows: the step of reconstructing the transition echo matrix according to the aperture-sparse based sparse measurement matrix to obtain a first echo matrix comprises:

[0031] According to the aperture-sparse based sparse measurement matrix, a compressed sensing reconstruction algorithm is used to reconstruct the data in each distance unit of the transition echo matrix, ie, each column vector, to form a first echo matrix.

[0032] A further technical solution of the present invention: the step of constructing a uniformly changing first rotation angle data grid comprises:

[0033] Performing polynomial fitting on the target track to obtain polynomial fitting parameters;

[0034] According to the polynomial fitting parameters, the geometric angle between the target point trace and the radar line of sight direction is calculated to obtain the target rotation angle data;

[0035] Based on the target rotation angle data range, a uniformly changing first rotation angle data grid is constructed.

[0036] A further technical solution of the present invention is as follows: interpolating the first echo matrix to obtain a second echo matrix according to the first turning angle data grid comprises:

[0037] According to the first turning angle data grid, grid interpolation is performed on the data in each distance unit of the first echo matrix, ie, each column vector, to form a second echo matrix.

[0038] A further technical solution of the present invention is as follows: the construction of the sparse measurement matrix based on angle sparseness and the second rotation angle data grid comprises:

[0039] According to the restored angle range, based on the rotation angle data grid, constructing an angle sparse based measurement matrix;

[0040] According to the restored angle range, a second rotation angle data grid is constructed.

[0041] A further technical solution of the present invention is as follows: the second echo matrix is ​​reconstructed according to the angle-sparse based sparse observation matrix to obtain a third echo matrix, comprising:

[0042] Based on the angle-sparse based sparse observation matrix, a compressed sensing reconstruction algorithm is used to reconstruct the data in each distance unit of the second echo matrix, that is, each column vector, to form a third echo matrix.

[0043] A further technical solution of the present invention: the step of obtaining a projection image of each row of data of the third echo matrix based on the coordinate mapping grid comprises:

[0044] According to the center point distance, the round-trip delay from the coordinate mapping grid point to the radar is calculated, and the data in each corner unit of the third echo matrix, that is, each row vector, is interpolated according to the round-trip delay of the coordinate mapping grid point to calculate the projection image of each row of data of the third echo matrix.

[0045] A further technical solution of the present invention is: obtaining the impact system ISAR imaging result based on the projection image comprises:

[0046] Superimposing the data in each corner unit of the third echo matrix, i.e., the projection image of each row vector, to obtain an original coherent accumulation image;

[0047] The original coherent accumulation image is subjected to high-pass filtering to eliminate the low-frequency clutter image caused by projection and obtain the impact system ISAR imaging result.

[0048] A further technical solution of the present invention is as follows: the impact radar includes the following features:

[0049] The impact radar is a multi-transmit and multi-receive antenna array with an angle measurement function;

[0050] The impulse radar transmits a Gaussian pulse with no carrier frequency;

[0051] The angle measurement range of the impact radar does not exceed 0 to 180 degrees.

[0052] The beneficial effects of the present invention are:

[0053] The present invention provides an impact system ISAR imaging method based on compressed sensing theory. The method performs polynomial fitting on the echo point traces through the echo matrix observed in azimuth to obtain a track curve; then solves the geometric relationship to obtain the corresponding rotation angle of each echo; then performs envelope alignment on the original echo data to construct a sparse measurement matrix and a sparse model; then, the sparsely observed target echo data are divided into a plurality of column vectors according to distance units to obtain reconstructed data; finally, interpolation and re-sampling operations are performed to obtain an impact system ISAR image.

[0054] Compared with the existing technology, its significant advantages are: the impact system ISAR radar adopts carrier-free Gaussian pulses, fully utilizes the antenna angle measurement function, solves geometric relationships without using traditional turntable imaging, and completes the non-cooperative target angle estimation through track polynomial fitting, thereby constructing an accurate coordinate mapping grid and completing the precise projection of the echo signal; at the same time, using the incomplete angle information of the non-cooperative target to construct a sparse measurement matrix, and through the compressed sensing recovery and reconstruction algorithm, the full coverage of the non-cooperative target echo angle information is achieved; the imaging method can, under the premise of lack of angle information and full angle coverage, construct sparse measurement matrices of different latitudes according to the echo and track characteristics, thereby restoring and reconstructing the complete echo signal, obtaining all the target information, and realizing high-quality imaging of the impact system ISAR imaging radar. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0056] Figure 1 It is a schematic diagram of simulation parameters of the impact system ISAR radar of the present invention.

[0057] Figure 2 It is a schematic diagram of the non-cooperative target scattering point model of the present invention.

[0058] Figure 3 Schematic diagram of the target track of the present invention.

[0059] Figure 4 It is a schematic diagram of the target point rotation angle calculation of the present invention.

[0060] Figure 5 It is the turning angle range corresponding to the target track of the present invention.

[0061] Figure 6 This is the data after envelope alignment of the original echo matrix YM×N of the present invention.

[0062] Figure 7 It is the original echo matrix YM×N of the present invention, which is directly projected and reconstructed without compressed sensing.

[0063] Figure 8 The echo data is reconstructed by angle sparse method of the present invention.

[0064] Fig. 9 This is a partial row projection image of the angle sparsely reconstructed echo data of the present invention.

[0065] Fig.10 It is the original coherent accumulation image of the angle sparsely reconstructed echo data of the present invention.

[0066] Fig.11 This is the ISAR imaging result of the angle-sparse impactor system of the present invention.

[0067] Fig.12 It is a schematic diagram of the aperture sparse target track of the present invention.

[0068] Fig.13 This is the data after envelope alignment of the aperture sparse original echo matrix XQ×P of the present invention.

[0069] Fig.14 It is the imaging result of directly projecting the aperture sparse original echo matrix XQ×P of the present invention without compressed sensing reconstruction.

[0070] Fig.15 The aperture sparsely reconstructed echo data of the present invention.

[0071] Fig.16 It is the target point trace under full aperture after aperture sparse reconstruction of the present invention.

[0072] Fig.17 This is the data reconstructed by the aperture+angle sparse compressed sensing of the present invention.

[0073] Fig.18 This is a partial row projection image of the aperture+angle sparsely reconstructed echo data of the present invention.

[0074] Fig.19 It is the original coherent accumulation image of the aperture+angle sparsely reconstructed echo data of the present invention.

[0075] Fig. 20 This is the ISAR imaging result of the aperture+angle sparse impactor system of the present invention.

[0076] Fig.21 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0077] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0078] like Figure 1As shown in FIG. 1 , simulation parameters of an impact system ISAR radar in an embodiment of the present invention are shown, including: Gaussian pulse width, pulse repetition period, radar scanning angle, number of scanning angles, non-cooperative target distance, and sampling rate parameters. The impact system ISAR radar emits Gaussian pulses, performs target detection within the scanning angle range, and obtains target angle and original echo data. The non-cooperative target scattering point model, such as Figure 2 shown.

[0079] Embodiment 1:

[0080] Specific implementation method 1: This implementation method describes an impact system ISAR imaging method based on compressed sensing theory. The impact system radar is a multi-transmit multi-receive antenna array with an angle measurement function; the impact system radar transmits a Gaussian pulse without a carrier frequency; and the angle measurement range does not exceed 0 to 180 degrees.

[0081] In the embodiment, an impact system ISAR imaging method based on compressed sensing theory is applicable to: Under normal circumstances, during continuous observation, an imaging method for impact system ISAR imaging of a remote non-cooperative target. The polynomial is used for track fitting to obtain the geometric angle of the point track, a sparse measurement matrix based on angle sparsity is constructed, the complete echo signal is restored, and the target mapping grid is obtained to reconstruct high-quality ISAR imaging results; the specific steps are as follows:

[0082] Step S01: Receive the echo data of the impact system radar, determine the radar system parameters, and obtain the target track information, such as Figure 3 As shown, the original echo matrix Y is formed M×N ;

[0083] Step S02, performing polynomial fitting on the target track to obtain polynomial fitting parameters;

[0084] In this embodiment, the polynomial equation for performing polynomial fitting on the target track is:

[0085] x=a0+a1(y-b1)+a2(y-b2) 2 +a3(y-b3) 3 +a4(y-b4) 4 +…

[0086] Among them, (a0, a1, a2, a3, a4, …), (b1, b2, b3, b4, …) are polynomial fitting parameters.

[0087] Step S03, calculating the geometric angle between the target point trace and the radar line of sight direction according to the polynomial fitting parameters, and obtaining the target rotation angle data;

[0088] In this embodiment, the rotation angle is calculated as follows: Figure 4As shown in the figure, the angle between the tangent line at a certain point on the track and the u-axis is α; the angle between the radar line of sight and the u-axis is β; the angle of the target relative to the radar line of sight is θ. The specific steps are as follows:

[0089] Step S001, calculate the radar track derivative according to the polynomial fitting parameters, the angle α between the tangent line at a certain point of the track and the u-axis is:

[0090] α=arctan(a1y+2a2(y-b2)+3a3(y-b3) 2 +4a4(y-b4) 3 +…);

[0091] Step S002, receiving echo data of the impact system radar, and obtaining radar sight angle information corresponding to the target track point;

[0092] Step S003: According to the geometric relationship, the target rotation angle θ relative to the radar line of sight can be expressed as:

[0093] θ=α-β-90°

[0094] In this embodiment, Figure 3 The corresponding turning angle range of each point on the target track is shown in Figure 5 .

[0095] Step S04: convert the original echo matrix Y M×N Align and center the envelope to obtain the center point distance R0;

[0096] In this embodiment, the original echo matrix Y M×N The envelope aligned data are shown in Figure 6 , the center point distance R0 = 200km. Original echo matrix Y M×N Without compressed sensing reconstruction, the imaging result is directly projected, such as Figure 7 As shown, the steps are as follows:

[0097] Step S0011: Obtain the echo matrix Y after envelope alignment according to the rotation angle data M×N The mapping grid of each row vector;

[0098] Step S0012, calculate the echo matrix Y M×N Each row maps the round-trip delay from each point in the grid to the radar, and the echo matrix Y M×N Each row is interpolated according to the round-trip delay of the mapped grid points to calculate the echo matrix Y M×N The projection image of each row of data.

[0099] Step S0013: The echo matrix Y M×N The projection images of each row are coherently superimposed to obtain the original coherent accumulation image;

[0100] Step S0014: high-pass filter the original coherent accumulation image to eliminate the low-frequency clutter image caused by projection, and obtain the imaging result of the impact system ISAR without compressed sensing reconstruction.

[0101] Step S05: construct a uniformly changing angle data grid according to the target angle data range, and transform the original echo matrix Y M×N The data in each range unit, i.e., each column vector, is grid interpolated to form a new echo matrix Y1. M1×N ;

[0102] Step S06: construct a sparse measurement matrix based on angle sparseness according to the angle data grid, and use the compressed sensing reconstruction algorithm to transform the echo matrix Y1 M1×N The data in each range unit, i.e., each column vector, is reconstructed to form the echo matrix Y2 M2×N ;

[0103] In this embodiment, the data reconstructed by compressed sensing is shown in Figure 8 , compressed sensing reconstruction uses the orthogonal matching pursuit algorithm, and the specific steps are as follows:

[0104] Step S0021, initialization: input measurement value y, measurement matrix φ, perception matrix A, sparsity K, initial residual r, number of iterations n;

[0105] Step S0022, find the subscript corresponding to the maximum value in the column product of the residual r and the perception matrix, and save it;

[0106] Step S0023, using the saved index to find a new index set from the perception matrix;

[0107] Step S0024, using the least square method to process the new index set and the new approximation value;

[0108] Step S0025, calculate the new residual r, the number of iterations n = n + 1;

[0109] Step S0026, determine whether the residual r is less than the set value, if so, exit the loop, if not, determine whether the number of iterations is greater than the sparsity, if so, stop, and save the sparsity K residual r at this time;

[0110] Step S0027, sparsity K=K+1, repeat steps S0012 to S0016;

[0111] Step S0028, determine the minimum residual within the sparsity K value range to obtain reconstructed data.

[0112] Step S07: construct a new corner data grid based on the sparse measurement matrix to obtain the echo matrix Y2 M2×N The data in each corner unit is the mapping grid of each row vector;

[0113] Step S08: Calculate the round-trip delay from each point of the mapping grid to the radar, and convert the echo matrix Y2 M2×N The data in each corner unit, i.e., each row vector, is interpolated according to the two-way delay of the mapped grid points to calculate the echo matrix Y2 M2×N The projection image of each row of data is I m2 .

[0114] In this embodiment, the projection image I of part of the row data m2 Shown in Fig. 9 .

[0115] Step S09: Echo matrix Y2 M2×N The data in each corner unit is the projection image I of each row vector m2 Perform coherent superposition to obtain the original coherent accumulation image I;

[0116] In this embodiment, the original coherent accumulation image I is shown in Fig.10 Compared with the imaging results without compressed sensing reconstruction ( Figure 7 ), it is obvious that the imaging results in the vertical direction of the center are richer and without any missing.

[0117] Step S010: high-pass filter the original coherent accumulation image I to eliminate the low-frequency clutter image caused by projection, and obtain the impact system ISAR imaging result II.

[0118] In this embodiment, the impact system ISAR imaging result II based on angle sparse reconstruction is shown in Fig.11 .

[0119] Embodiment 2:

[0120] Specific implementation method 2, another impact system ISAR imaging method based on compressed sensing theory is described in this implementation method. Among them, the impact system radar is a multi-transmit multi-receive antenna array with an angle measurement function; the impact system radar transmits a Gaussian pulse without a carrier frequency; and the angle measurement range does not exceed 0 to 180 degrees.

[0121] In the embodiment, another impact system ISAR imaging method based on compressed sensing theory is applicable to: impact system ISAR imaging method when azimuth echo data is missing and observation of remote non-cooperative targets is discontinuous; polynomials are used for track fitting to obtain point track geometric angles, sparse measurement matrices based on aperture sparsity and angle sparsity are constructed respectively, complete echo signals are restored, and target mapping grids are obtained to reconstruct high-quality ISAR imaging results; the specific steps are as follows:

[0122] Step S11, receiving the echo data of the impact system radar, determining the radar system parameters, and obtaining the target track information, such as Fig.12 As shown, the original echo matrix X is formed Q×P;

[0123] Step S12: convert the original echo matrix X Q×P Align and center the envelope to obtain the center point distance R0;

[0124] In this embodiment, the original echo matrix X Q×P The envelope aligned data are shown in Fig.13 , the center point distance R0 = 200km. Aperture sparse original echo matrix X Q×P Without compressed sensing reconstruction, the imaging result is directly projected, such as Fig.14 As shown, the steps are as follows:

[0125] Step S1001: Obtain the echo matrix X after envelope alignment according to the rotation angle data Q×P The mapping grid of each row vector;

[0126] Step S1002: Calculate the echo matrix X Q×P Each row maps the round-trip delay from each point in the grid to the radar, and the echo matrix X Q×P Each row is interpolated according to the round-trip delay of the mapped grid points to calculate the echo matrix X Q×P The projection image of each row of data.

[0127] Step S1003: transform the echo matrix X Q×P The projection images of each row are coherently superimposed to obtain the original coherent accumulation image;

[0128] Step S1004: high-pass filter the original coherent accumulation image to eliminate the low-frequency clutter image caused by projection, and obtain the imaging result of the impact system ISAR without compressed sensing reconstruction under sparse aperture.

[0129] Step S13: construct a sparse measurement matrix based on the sparse aperture, and use a compressed sensing reconstruction algorithm to transform the original echo matrix X Q×P The data in each range unit, i.e., each column vector, is reconstructed to form the echo matrix X1 Q1×P ;

[0130] In this embodiment, the data after sparse aperture reconstruction by compressed sensing is shown in Fig.15 , compressed sensing reconstruction uses the orthogonal matching pursuit algorithm, and the specific steps are as follows:

[0131] Step S1011, initialization: input measurement value y, measurement matrix φ, perception matrix A, sparsity K, initial residual r, number of iterations n;

[0132] Step S1012, find the subscript corresponding to the maximum value in the column product of the residual r and the perception matrix, and save it;

[0133] Step S1013, using the saved index to find a new index set from the perception matrix;

[0134] Step S1014: Process the new index set and the new approximation value using the least square method;

[0135] Step S1015, calculate a new residual r, the number of iterations n = n + 1;

[0136] Step S1016, determine whether the residual r is less than the set value, if so, exit the loop, if not, determine whether the number of iterations is greater than the sparsity, if so, stop, and save the sparsity K residual r at this time;

[0137] Step S1017, sparsity K=K+1, repeat steps S1012 to S1016;

[0138] Step S1018: determine the minimum residual within the sparsity K value range to obtain reconstructed data.

[0139] Step S14, performing polynomial fitting on the target track under the sparse aperture to obtain polynomial fitting parameters, and calculating the intersection of the radar line of sight at the sparse aperture and the target polynomial track;

[0140] In this embodiment, the target point trace under full aperture is as follows: Fig.16 As shown in the figure, the polynomial equation for polynomial fitting of the target track is:

[0141] x=a0+a1(y-b1)+a2(y-b2) 2 +a3(y-b3) 3 +a4(y-b4) 4 +…

[0142] Among them, (a0, a1, a2, a3, a4, …), (b1, b2, b3, b4, …) are polynomial fitting parameters.

[0143] Step S15, according to the target point trace under full aperture, calculate the geometric angle between it and the radar line of sight direction to obtain the target rotation angle data;

[0144] Step S16: construct a uniformly changing angle data grid according to the target angle data range, and transform the echo matrix X1 Q1×P The data in each range unit, i.e., each column vector, is grid interpolated to form a new echo matrix X2. Q2×P ;

[0145] Step S17: construct a sparse measurement matrix based on angle sparseness according to the angle data grid, and use the compressed sensing reconstruction algorithm to transform the echo matrix X2 Q2×P The data in each range unit, i.e., each column vector, is reconstructed to form the echo matrix X3 Q3×P;

[0146] In this embodiment, the data after the sparse angle is reconstructed by compressed sensing is shown in Fig.17 , compressed sensing reconstruction uses the orthogonal matching pursuit algorithm, and the specific steps are as follows:

[0147] Step S1021, initialization: input measurement value y, measurement matrix φ, perception matrix A, sparsity K, initial residual r, number of iterations n;

[0148] Step S1022, find the subscript corresponding to the maximum value in the column product of the residual r and the perception matrix, and save it;

[0149] Step S1023, using the saved index to find a new index set from the perception matrix;

[0150] Step S1024, using the least square method to process the new index set and the new approximation value;

[0151] Step S1025, calculate a new residual r, the number of iterations n = n + 1;

[0152] Step S1026, determine whether the residual r is less than the set value, if so, exit the loop, if not, determine whether the number of iterations is greater than the sparsity, if so, stop, and save the sparsity K residual r at this time;

[0153] Step S1027, sparsity K=K+1, repeat steps S1022 to S1026;

[0154] Step S1028, determine the minimum residual within the sparsity K value range to obtain reconstructed data.

[0155] Step S18: construct a new corner data grid based on the sparse measurement matrix to obtain the echo matrix X3 Q3×P The data in each corner unit is the mapping grid of each row vector;

[0156] Step S19, calculate the round-trip delay from each point of the mapping grid to the radar, and convert the echo matrix X3 Q3×P The data in each corner unit, i.e., each row vector, is interpolated according to the two-way delay of the grid point to calculate the echo matrix X3 Q3×P The projection image of each row of data is I q3 .

[0157] In this embodiment, the projection image I of part of the row data q3 Shown in Fig.18 .

[0158] Step S110: The echo matrix X3 Q3×P The data in each corner unit is the projection image I of each row vector q3 Perform coherent superposition to obtain the original coherent accumulation image I;

[0159] In this embodiment, the original coherent accumulation image I is shown in Fig.19 Compared with the imaging results without aperture + angle sparse reconstruction ( Fig.14 ), it is obvious that the imaging results in the vertical direction of the center are richer and without any missing.

[0160] Step S111: high-pass filter the original coherent accumulation image I to eliminate the low-frequency clutter caused by projection, and obtain the impact system ISAR imaging result II.

[0161] In this embodiment, the impact system ISAR imaging result II based on aperture + angle sparse reconstruction is shown in Fig. 20 .

[0162] In summary, the present invention utilizes the antenna angle measurement function to perform polynomial fitting on the non-cooperative target point traces, thereby completing the target angle estimation and accurately constructing the coordinate mapping grid; then using the incomplete angle information, a sparse measurement matrix is ​​constructed to reconstruct the target echo at all angles to achieve full angle coverage. The imaging method can construct sparse measurement matrices of different latitudes according to the echo and track characteristics in the case of angle or aperture + angle sparseness, restore and reconstruct the complete echo signal, obtain all the target information, and achieve high-quality imaging of the impact system ISAR imaging radar.

[0163] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for ISAR imaging based on compressed sensing theory, characterized in that: include: Receive the echo data of the impact system radar, determine the radar system parameters, obtain the target track information, and form the original echo matrix; Envelope aligning and centering the original echo matrix to form a first echo matrix, and obtaining a center point distance; Constructing a uniformly varying first turning angle data grid; According to the first turning angle data grid, interpolating the first echo matrix to obtain a second echo matrix; Construct a sparse observation matrix based on angle sparsity and a second rotation angle data grid; Reconstructing the second echo matrix according to the sparse measurement matrix to obtain a third echo matrix; According to the second turning angle data grid, obtaining a coordinate mapping grid corresponding to each point of the second turning angle data grid; Obtaining a projection image of the third echo matrix based on the coordinate mapping grid; The impact system ISAR imaging result is obtained based on the projection image.

2. A method for ISAR imaging based on compressed sensing theory, characterized in that: include: Receive the echo data of the impact system radar, determine the radar system parameters, obtain the target track information, and form the original echo matrix; Envelope aligning and centering the original echo matrix to form a transition echo matrix, and obtaining a center point distance; Constructing a sparse measurement matrix based on aperture sparsity according to the sparse aperture; Reconstructing the transition echo matrix according to the aperture-sparse based sparse measurement matrix to obtain a first echo matrix; Constructing a uniformly varying first turning angle data grid; According to the first turning angle data grid, interpolating the first echo matrix to obtain a second echo matrix; Construct a sparse observation matrix based on angle sparsity and a second rotation angle data grid; Reconstructing the second echo matrix according to the angle-sparse based sparse measurement matrix to obtain a third echo matrix; According to the second turning angle data grid, obtaining a coordinate mapping grid corresponding to each point of the second turning angle data grid; Obtaining a projection image of the third echo matrix based on the coordinate mapping grid; The impact system ISAR imaging result is obtained based on the projection image.

3. The method for ISAR imaging based on compressed sensing theory according to claim 2, characterized in that: The reconstructing the transition echo matrix according to the aperture-sparse based sparse measurement matrix to obtain a first echo matrix includes: According to the aperture-sparse based sparse measurement matrix, a compressed sensing reconstruction algorithm is used to reconstruct the data in each distance unit of the transition echo matrix, ie, each column vector, to form a first echo matrix.

4. The method for ISAR imaging based on compressed sensing theory according to claim 1 or 2, characterized in that: The step of constructing a uniformly changing first rotation angle data grid comprises: Performing polynomial fitting on the target track to obtain polynomial fitting parameters; According to the polynomial fitting parameters, the geometric angle between the target point trace and the radar line of sight direction is calculated to obtain the target rotation angle data; Based on the target rotation angle data range, a uniformly changing first rotation angle data grid is constructed.

5. The method for ISAR imaging based on compressed sensing theory according to claim 1 or 2, characterized in that: The interpolating the first echo matrix to obtain a second echo matrix according to the first turning angle data grid includes: According to the first turning angle data grid, grid interpolation is performed on the data in each distance unit of the first echo matrix, ie, each column vector, to form a second echo matrix.

6. The method for ISAR imaging based on compressed sensing theory according to claim 1 or 2, characterized in that: The constructing of the angle-sparse based sparse measurement matrix and the second rotation angle data grid includes: According to the restored angle range, based on the rotation angle data grid, constructing an angle sparse based measurement matrix; According to the restored angle range, a second rotation angle data grid is constructed.

7. The method for ISAR imaging based on compressed sensing theory according to claim 1 or 2, characterized in that: The reconstructing the second echo matrix to obtain a third echo matrix according to the angle-sparse based sparse observation matrix includes: Based on the angle-sparse based sparse observation matrix, a compressed sensing reconstruction algorithm is used to reconstruct the data in each distance unit of the second echo matrix, that is, each column vector, to form a third echo matrix.

8. The method for ISAR imaging based on compressed sensing theory according to claim 1 or 2, characterized in that: The step of obtaining a projection image of each row of data of the third echo matrix based on the coordinate mapping grid comprises: According to the center point distance, the round-trip delay from the coordinate mapping grid point to the radar is calculated, and the data in each corner unit of the third echo matrix, that is, each row vector, is interpolated according to the round-trip delay of the coordinate mapping grid point to calculate the projection image of each row of data of the third echo matrix.

9. The method for ISAR imaging based on compressed sensing theory according to claim 1 or 2, characterized in that: The step of obtaining the impact system ISAR imaging result based on the projection image comprises: Superimposing the data in each corner unit of the third echo matrix, i.e., the projection image of each row vector, to obtain an original coherent accumulation image; The original coherent accumulation image is subjected to high-pass filtering to eliminate the low-frequency clutter image caused by projection and obtain the impact system ISAR imaging result.

10. The method for ISAR imaging based on compressed sensing theory according to claim 1 or 2, characterized in that: The impact radar includes the following features: The impact radar is a multi-transmit and multi-receive antenna array with an angle measurement function; The impulse radar transmits a Gaussian pulse with no carrier frequency; The angle measurement range of the impact radar does not exceed 0 to 180 degrees.

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