Ultra-wideband radar super-resolution imaging method based on spectrum mode decomposition

Abstract

The invention discloses an ultra-wideband radar super-resolution imaging method based on spectrum mode decomposition. The method comprises the following steps: acquiring radar observation data; constructing a radar observation data vector and a frequency dependence factor set, and defining a frequency spectrum mode matrix for each frequency dependence factor, so as to obtain a Toeplitz matrix and a single-mode frequency spectrum vector corresponding to an optimal solution through solving a convex optimization problem; estimating scattering parameters of each scattering center in the scene according to the Toeplitz matrix and the single-mode spectrum vector; and carrying out spectrum extrapolation and radar image calculation according to the scattering parameters so as to output a super-resolution radar image of the target according to a calculation result. The radar spectrum is subjected to mode decomposition according to different frequency dependence factors, the estimation accuracy of target parameters is improved, the spectrum extrapolation precision under the large frequency span is improved, and then the radar super-resolution imaging quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing, and in particular to an ultra-wideband radar super-resolution imaging method based on spectrum mode decomposition. Background Art

[0002] Ultra-wideband radar imaging technology is widely used in geological exploration, smart healthcare, intelligent security, and other fields. Spectral extrapolation, based on the geometric diffraction theory model, is an important super-resolution imaging algorithm that improves imaging resolution by increasing the equivalent bandwidth. While ultra-wideband radar imaging technology has widespread applications in various fields, existing technologies suffer from insufficient spectral extrapolation accuracy over large frequency spans, limiting imaging quality. Summary of the Invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] The present invention proposes an ultra-wideband radar super-resolution imaging method based on spectral mode decomposition, which is superior to the existing technology in terms of spectrum extrapolation accuracy, target parameter estimation accuracy and imaging quality, and has strong adaptability and efficient computing power.

[0005] Another object of the present invention is to provide an ultra-wideband radar super-resolution imaging system based on spectral mode decomposition.

[0006] To achieve the above objectives, the present invention provides, on one hand, an ultra-wideband radar super-resolution imaging method based on spectral mode decomposition, comprising:

[0007] Acquire radar observation data;

[0008] Construct a radar observation data vector and a set of frequency-dependent factors, and define a spectrum mode matrix for each frequency-dependent factor to obtain the Toeplitz matrix and single-mode spectrum vector corresponding to the optimal solution by solving a convex optimization problem.

[0009] Estimate the scattering parameters of each scattering center in the scene based on the Toeplitz matrix and the single-mode spectrum vector;

[0010] Spectrum extrapolation and radar image calculation are performed based on the scattering parameters to output a super-resolution radar image of the target based on the calculation results.

[0011] The ultra-wideband radar super-resolution imaging method based on spectral mode decomposition according to an embodiment of the present invention may also have the following additional technical features:

[0012] In one embodiment of the present invention, obtaining radar observation data includes:

[0013] Determine the frequency range of the radar full band to output the frequency point set of the radar full band;

[0014] Selecting a subset from the frequency point set of the radar full frequency band as the radar observation frequency band, and outputting the frequency point set of the radar observation frequency band;

[0015] The target's reflected signal is collected at each frequency point in the radar observation band to obtain radar observation data.

[0016] In one embodiment of the present invention, a radar observation data vector and a set of frequency dependency factors are constructed, and a spectrum pattern matrix is defined for each frequency dependency factor, including:

[0017] constructing a radar observation data vector by arranging the radar observation data into a vector;

[0018] Define the set of frequency-dependent factors in the geometric diffraction theory model;

[0019] A spectral pattern matrix is defined for each frequency dependent factor in the set of frequency dependent factors.

[0020] In one embodiment of the present invention, estimating the scattering parameters of each scattering center in a scene based on a Toeplitz matrix and a single-mode spectrum vector includes:

[0021] Perform Vandermonde decomposition on the Toeplitz matrix to obtain the Vandermonde matrix;

[0022] Calculate the estimated distance parameter of the scattering center corresponding to the frequency dependence factor based on the Vandermonde matrix;

[0023] Calculate the coefficient vector according to the Vandermonde matrix and the single-mode spectrum vector, and calculate the amplitude parameter estimate of the frequency-dependent factor corresponding to the scattering center according to the distance parameter estimate;

[0024] A single-mode scattering parameter set is calculated based on the distance parameter estimate and the amplitude parameter estimate, scattering centers with amplitude parameter estimates greater than a preset threshold are retained in the single-mode scattering parameter set, and a combined scattering parameter set is calculated.

[0025] In one embodiment of the present invention, spectrum extrapolation and radar image calculation are performed based on scattering parameters to output a super-resolution radar image of the target based on the calculation results, including:

[0026] Based on the combined scattering parameter set, the full-band spectrum estimate is calculated according to the geometric diffraction theory model;

[0027] Calculate the super-resolution range image of the target based on the full-band spectrum estimation value and the Fourier transform;

[0028] Based on the super-resolution range image of the target from multiple perspectives, the super-resolution two-dimensional image or three-dimensional image is calculated according to the radar system and geometric relationship.

[0029] To achieve the above-mentioned object, the present invention further provides an ultra-wideband radar super-resolution imaging system based on spectral mode decomposition, comprising:

[0030] Radar data acquisition module, used to obtain radar observation data;

[0031] The spectrum pattern decomposition module is used to construct the radar observation data vector and the frequency-dependent factor set, and define the spectrum pattern matrix for each frequency-dependent factor. The Toeplitz matrix and single-mode spectrum vector corresponding to the optimal solution are obtained by solving the convex optimization problem.

[0032] Scattering parameter estimation module, used to estimate the scattering parameters of each scattering center in the scene based on the Toeplitz matrix and the single-mode spectrum vector;

[0033] The super-resolution radar image output module is used to perform spectrum extrapolation and radar image calculation based on scattering parameters, and output a super-resolution radar image of the target based on the calculation results.

[0034] The ultra-wideband radar super-resolution imaging method and system based on spectral mode decomposition in the embodiments of the present invention decomposes the radar spectrum into modes according to different frequency-dependent factors, thereby improving the accuracy of target parameter estimation and the precision of spectrum extrapolation under large frequency spans, thereby improving the quality of radar super-resolution imaging.

[0035] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0037] Figure 1 is a flow chart of an ultra-wideband radar super-resolution imaging method based on spectral mode decomposition according to an embodiment of the present invention;

[0038] Figure 2 is a comparison chart of spectrum extrapolation accuracy according to an embodiment of the present invention and an existing method;

[0039] Figure 3 is a comparison chart of distance parameter estimation accuracy according to an embodiment of the present invention and an existing method;

[0040] Figure 4 is a comparison diagram of imaging results according to an embodiment of the present invention and an existing method;

[0041] Figure 5 4 is a structural diagram of an ultra-wideband radar super-resolution imaging system based on spectral mode decomposition according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0043] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0044] The following describes an ultra-wideband radar super-resolution imaging method and system based on spectral mode decomposition according to an embodiment of the present invention with reference to the accompanying drawings.

[0045] Figure 1 FIG. 1 is a flow chart of an ultra-wideband radar super-resolution imaging method based on spectrum mode decomposition according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0046] S1, obtain radar observation data.

[0047] Specifically, the frequency range of the radar full-band is determined to output a frequency point set of the radar full-band; a subset is selected from the frequency point set of the radar full-band as the radar observation frequency band, and the frequency point set of the radar observation frequency band is output; and the reflected signal of the target is collected at each frequency point of the radar observation frequency band to obtain radar observation data.

[0048] In the embodiment of the present invention, the radar full-band {f m :m=0,…,M-1}, there are M frequency points f m Composition, frequency interval Δf, satisfying f m =f0+mΔf.

[0049] In the embodiment of the present invention, the radar observation frequency band is composed of some frequency points in the full frequency band, and the frequency point subscript set is N is the number of elements in the set Ω, satisfying N≤M.

[0050] In the embodiment of the present invention, radar observation data

[0051] S2, constructs the radar observation data vector and the frequency dependence factor set, and defines the spectrum mode matrix for each frequency dependence factor, so as to obtain the Toeplitz matrix and single-mode spectrum vector corresponding to the optimal solution by solving the convex optimization problem.

[0052] Specifically, the radar observation data at each azimuth angle is processed as follows. The radar observation data vector is defined as That is, it is constructed by arranging radar observation data into vectors. The frequency dependent factor set in the geometric diffraction theory model is defined as S = {α1, α2, ..., α J}, usually J = 5, α∈{-1,-1 / 2,0,1 / 2,1}. For each frequency dependence factor α∈S, define the spectrum mode matrix is the following diagonal matrix:

[0053]

[0054] Among them, diag(x0,…,x M-1 ) means the diagonal elements are x0,…,x M-1 The specific steps of spectral mode decomposition are as follows. Solve the following convex optimization problem:

[0055]

[0056] The optimized variables are in is called a single-mode spectrum vector, τ>0 is the regularization coefficient, Represents the Toeplitz operator, which is used to transform the M-dimensional vector x=[x0,…,x M-1 ] T The mapping is an M-order square matrix, defined as:

[0057]

[0058] Tr(·) represents the trace of the matrix. The nth row and mth column element of is defined as:

[0059]

[0060] (·) * represents the conjugate transpose of the matrix, (·) ≥ 0 means the matrix is semi-positive definite. The value of the regularization coefficient τ can be set according to the noise level of the application scenario. When the standard σ of the radar receiving noise is known, it can be set to Convex optimization problems can be solved using a variety of convex optimization algorithms, for example, using the CVX toolkit.

[0061] S3, estimates the scattering parameters of each scattering center in the scene based on the Toeplitz matrix and the single-mode spectrum vector.

[0062] Specifically, according to the Toeplitz matrix T(u α ), single-mode spectrum vector xα , Estimate the scattering parameters of each scattering center in the scene. The specific steps are as follows:

[0063] Vandermonde decomposition of the Toeplitz matrix. For each frequency-dependent factor α∈S, the Toeplitz matrix T(u α ) is subjected to Vandermonde decomposition, and the expression is:

[0064]

[0065] in diagonal matrix, is the Vandermonde matrix, and its expression is:

[0066]

[0067] in, Satisfaction|v α,l |=1.

[0068] Distance parameter estimation. For each frequency-dependent factor α∈S, according to the Vandermonde matrix V α , calculate the distance parameter estimate r of the lth scattering center corresponding to the frequency dependence factor α α,l , the expression is as follows:

[0069]

[0070] Where ∠x∈[0,2π) represents the argument of the complex number x, R=c / (2Δf) represents the maximum unambiguous distance, and c represents the speed of light.

[0071] Amplitude parameter estimation. For each frequency-dependent factor α∈S, according to the Vandermonde matrix V α and the single-mode spectrum vector x α , calculate the coefficient vector The expression is:

[0072]

[0073] Calculate the amplitude parameter estimate σ of the lth scattering center corresponding to the frequency dependence factor α α,l , l=0,…,M-1, the expression is:

[0074]

[0075] in, Represents an imaginary unit.

[0076] Scattering parameter merging. For each frequency-dependent factor α∈S, calculate the single-mode scattering parameter set P α , defined as:

[0077] Pα={(α,rα ,l ,σα ,l ): l=0,…,M-1} (10)

[0078] In P α The scattering centers with larger amplitude parameter estimates are retained.

[0079]

[0080] Among them, T α Indicates the amplitude threshold, for example, it can be set to Calculate the combined scattering parameter set P, defined as:

[0081]

[0082] S4, performing spectrum extrapolation and radar image calculation according to the scattering parameters, and outputting a super-resolution radar image of the target according to the calculation results.

[0083] Specifically, spectrum extrapolation and radar image calculation are performed based on the combined scattering parameter estimation value set P. The specific steps are as follows:

[0084] Calculate the full-band spectrum estimate based on the geometric diffraction theory model The expression is:

[0085]

[0086] The summation range is (α n ,r n ,σ n )∈P,N s Represents the number of elements in the set P. Then, based on Fourier transform, the super-resolution range image S(r) of the target is calculated, r∈[0,R), and the expression is:

[0087]

[0088] Based on the multi-view super-resolution range image, a super-resolution 2D or 3D image can be calculated according to the radar system and geometric relationship. For example, a super-resolution 2D image can be calculated for an ISAR radar.

[0089] Further, Figure 1 The spectral extrapolation accuracy of the proposed method is compared with existing methods, measured by root mean square error (RMSE) at signal-to-noise ratios (SNRs) of 10dB, 15dB, 20dB, 25dB, and 30dB. ANM-MD is the proposed method, while support vector regression (SVR), generalized likelihood ratio test (GLRT), and atomic norm optimization (ANM) are existing methods. The proposed method achieves higher spectral extrapolation accuracy than existing methods.

[0090] Further, Figure 2 The accuracy of distance parameter estimation of the proposed method is compared with that of existing methods, measured by the root mean square error (RMSE). represents the arithmetic square root of the Cramer-Rao bound (i.e., the lower bound of the RMSE for distance parameter estimation), with signal-to-noise ratios (SNRs) of 10dB, 15dB, 20dB, 25dB, and 30dB. ANM-MD represents the proposed method, while support vector regression (SVR), generalized likelihood ratio test (GLRT), and atomic norm optimization (ANM) represent existing methods. The curves for SVR and GLRT overlap. The proposed method achieves higher distance parameter estimation accuracy than existing methods and is closer to the Cramer-Rao bound.

[0091] Further, Figure 3 Comparison of imaging results between the proposed method and existing methods. The root mean square error (RMSE) is indicated in the legend. The signal-to-noise ratio (SNR) is 15dB. ANM-MD represents the proposed method, while support vector regression (SVR), generalized likelihood ratio test (GLRT), and atomic norm optimization (ANM) represent existing methods. The proposed method achieves the smallest RMSE, and the super-resolved range image is closest to the ground truth.

[0092] According to an embodiment of the present invention, the ultra-wideband radar super-resolution imaging method based on spectral pattern decomposition performs pattern decomposition of the radar spectrum according to different frequency-dependent factors. This method can more accurately estimate spectral characteristics. Through spectral pattern decomposition and Vandermonde decomposition, the distance and amplitude parameters of the scattering center can be more accurately estimated. This makes the imaging results closer to the true value, improving the accuracy of target detection and recognition. Based on spectral pattern decomposition and optimized spectral extrapolation, this method can generate clearer and more accurate super-resolution radar images. Through the flexible setting of the regularization coefficient, it can adapt to the noise level of different application scenarios. When the radar receiver noise standard is known, the regularization coefficient can be optimized to further improve the imaging quality.

[0093] In order to implement the above embodiment, Figure 5 As shown, this embodiment also provides an ultra-wideband radar super-resolution imaging system 10 based on spectrum mode decomposition, including:

[0094] Radar data acquisition module 100, used to acquire radar observation data;

[0095] The spectrum mode decomposition module 200 is used to construct a radar observation data vector and a set of frequency-dependent factors, and define a spectrum mode matrix for each frequency-dependent factor, so as to obtain a Toeplitz matrix and a single-mode spectrum vector corresponding to the optimal solution by solving a convex optimization problem;

[0096] a scattering parameter estimation module 300 for estimating the scattering parameters of each scattering center in the scene based on the Toeplitz matrix and the single-mode spectrum vector;

[0097] The super-resolution radar image output module 400 is used to perform spectrum extrapolation and radar image calculation according to the scattering parameters, and output a super-resolution radar image of the target according to the calculation results.

[0098] Furthermore, the radar data acquisition module 100 is further configured to:

[0099] Determine the frequency range of the radar full band to output the frequency point set of the radar full band;

[0100] Selecting a subset from the frequency point set of the radar full frequency band as the radar observation frequency band, and outputting the frequency point set of the radar observation frequency band;

[0101] The target's reflected signal is collected at each frequency point in the radar observation band to obtain radar observation data.

[0102] Furthermore, the spectrum mode decomposition module 200 is further configured to:

[0103] constructing a radar observation data vector by arranging the radar observation data into a vector;

[0104] Define the set of frequency-dependent factors in the geometric diffraction theory model;

[0105] A spectral pattern matrix is defined for each frequency dependent factor in the set of frequency dependent factors.

[0106] Furthermore, the scattering parameter estimation module 300 is further configured to:

[0107] Perform Vandermonde decomposition on the Toeplitz matrix to obtain the Vandermonde matrix;

[0108] Calculate the estimated distance parameter of the scattering center corresponding to the frequency dependence factor based on the Vandermonde matrix;

[0109] Calculate the coefficient vector according to the Vandermonde matrix and the single-mode spectrum vector, and calculate the amplitude parameter estimate of the frequency-dependent factor corresponding to the scattering center according to the distance parameter estimate;

[0110] A single-mode scattering parameter set is calculated based on the distance parameter estimate and the amplitude parameter estimate, scattering centers with amplitude parameter estimates greater than a preset threshold are retained in the single-mode scattering parameter set, and a combined scattering parameter set is calculated.

[0111] Furthermore, the super-resolution radar image output module 400 is further configured to:

[0112] Based on the combined scattering parameter set, the full-band spectrum estimate is calculated according to the geometric diffraction theory model;

[0113] Calculate the super-resolution range image of the target based on the full-band spectrum estimation value and the Fourier transform;

[0114] Based on the super-resolution range image of the target from multiple perspectives, the super-resolution two-dimensional image or three-dimensional image is calculated according to the radar system and geometric relationship.

[0115] According to an embodiment of the present invention, an ultra-wideband radar super-resolution imaging system based on spectral mode decomposition performs mode decomposition of the radar spectrum according to different frequency-dependent factors. This method can more accurately estimate spectral characteristics. Spectral mode decomposition and Vandermonde decomposition can more accurately estimate the distance and amplitude parameters of the scattering center. This makes the imaging results closer to the true value, improving the accuracy of target detection and recognition. Based on spectral mode decomposition and optimized spectral extrapolation, this method can generate clearer and more accurate super-resolution radar images. Through flexible setting of the regularization coefficient, it can adapt to the noise level of different application scenarios. When the radar receiver noise standard is known, the regularization coefficient can be optimized to further improve imaging quality.

[0116] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean 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 can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0117] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

Claims

1. A method for ultra-wideband radar super-resolution imaging based on spectral mode decomposition, characterized in that: include: Acquire radar observation data; Construct a radar observation data vector and a set of frequency-dependent factors, and define a spectrum mode matrix for each frequency-dependent factor to obtain the Toeplitz matrix and single-mode spectrum vector corresponding to the optimal solution by solving a convex optimization problem. Estimate the scattering parameters of each scattering center in the scene based on the Toeplitz matrix and the single-mode spectrum vector; Spectrum extrapolation and radar image calculation are performed based on the scattering parameters to output a super-resolution radar image of the target based on the calculation results.

2. The method according to claim 1, characterized in that Acquire radar observation data, including: Determine the frequency range of the radar full band to output the frequency point set of the radar full band; Selecting a subset from the frequency point set of the radar full frequency band as the radar observation frequency band, and outputting the frequency point set of the radar observation frequency band; The target's reflected signal is collected at each frequency point in the radar observation band to obtain radar observation data.

3. The method according to claim 1, characterized in that Construct the radar observation data vector and the frequency dependency factor set, and define the spectrum pattern matrix for each frequency dependency factor, including: constructing a radar observation data vector by arranging the radar observation data into a vector; Define the set of frequency-dependent factors in the geometric diffraction theory model; A spectral pattern matrix is defined for each frequency dependent factor in the set of frequency dependent factors.

4. The method according to claim 1, wherein Estimate the scattering parameters of each scattering center in the scene based on the Toeplitz matrix and the single-mode spectrum vector, including: Perform Vandermonde decomposition on the Toeplitz matrix to obtain the Vandermonde matrix; Calculate the estimated distance parameter of the scattering center corresponding to the frequency dependence factor based on the Vandermonde matrix; Calculate the coefficient vector according to the Vandermonde matrix and the single-mode spectrum vector, and calculate the amplitude parameter estimate of the frequency-dependent factor corresponding to the scattering center according to the distance parameter estimate; A single-mode scattering parameter set is calculated based on the distance parameter estimate and the amplitude parameter estimate, scattering centers with amplitude parameter estimates greater than a preset threshold are retained in the single-mode scattering parameter set, and a combined scattering parameter set is calculated.

5. The method according to claim 4, characterized in that Spectrum extrapolation and radar image calculation are performed based on the scattering parameters to output a super-resolution radar image of the target based on the calculation results, including: Based on the combined scattering parameter set, the full-band spectrum estimate is calculated according to the geometric diffraction theory model; Calculate the super-resolution range image of the target based on the full-band spectrum estimation value and the Fourier transform; Based on the super-resolution range image of the target from multiple perspectives, the super-resolution two-dimensional image or three-dimensional image is calculated according to the radar system and geometric relationship.

6. An ultra-wideband radar super-resolution imaging system based on spectral mode decomposition, characterized in that: include: Radar data acquisition module, used to obtain radar observation data; The spectrum pattern decomposition module is used to construct the radar observation data vector and the frequency-dependent factor set, and define the spectrum pattern matrix for each frequency-dependent factor. The Toeplitz matrix and single-mode spectrum vector corresponding to the optimal solution are obtained by solving the convex optimization problem. Scattering parameter estimation module, used to estimate the scattering parameters of each scattering center in the scene based on the Toeplitz matrix and the single-mode spectrum vector; The super-resolution radar image output module is used to perform spectrum extrapolation and radar image calculation based on scattering parameters, and output a super-resolution radar image of the target based on the calculation results.

7. The system according to claim 6, characterized in that The radar data acquisition module is also used to: Determine the frequency range of the radar full band to output the frequency point set of the radar full band; Selecting a subset from the frequency point set of the radar full frequency band as the radar observation frequency band, and outputting the frequency point set of the radar observation frequency band; The target's reflected signal is collected at each frequency point in the radar observation band to obtain radar observation data.

8. The system according to claim 6, wherein: The spectrum pattern decomposition module is also used to: constructing a radar observation data vector by arranging the radar observation data into a vector; Define the set of frequency-dependent factors in the geometric diffraction theory model; A spectral pattern matrix is defined for each frequency dependent factor in the set of frequency dependent factors.

9. The system according to claim 6, wherein: The scattering parameter estimation module is also used to: Perform Vandermonde decomposition on the Toeplitz matrix to obtain the Vandermonde matrix; Calculate the estimated distance parameter of the scattering center corresponding to the frequency dependence factor based on the Vandermonde matrix; Calculate the coefficient vector according to the Vandermonde matrix and the single-mode spectrum vector, and calculate the amplitude parameter estimate of the frequency-dependent factor corresponding to the scattering center according to the distance parameter estimate; A single-mode scattering parameter set is calculated based on the distance parameter estimate and the amplitude parameter estimate, scattering centers with amplitude parameter estimates greater than a preset threshold are retained in the single-mode scattering parameter set, and a combined scattering parameter set is calculated.

10. The system according to claim 9, characterized in that The super-resolution radar image output module is also used for: Based on the combined scattering parameter set, the full-band spectrum estimate is calculated according to the geometric diffraction theory model; Calculate the super-resolution range image of the target based on the full-band spectrum estimation value and the Fourier transform; Based on the super-resolution range image of the target from multiple perspectives, the super-resolution two-dimensional image or three-dimensional image is calculated according to the radar system and geometric relationship.

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