MIMO imaging radar measurement matrix design method and system based on block sparse constraint

Abstract

The invention discloses a block sparse constraint-based MIMO imaging radar measurement matrix design method and system, and relates to the technical field of radar imaging, and the method comprises the following steps: obtaining the related parameters of a one-dimensional linear array in an MIMO radar, and obtaining the related parameters of the one-dimensional linear array in the MIMO radar; constructing a transmitted waveform matrix, and determining a target echo; carrying out measurement matrix design by considering the block sparsity of the target range profile; performing compression measurement on the target echo by using the measurement matrix based on a compressed sensing method to obtain a compressed target echo; and recovering a target range profile from the compressed target echo by using a block sparse recovery algorithm, and reconstructing a two-dimensional image by using a sparse recovery algorithm. According to the method, the target distance image block sparsity is considered, the weighted coherent minimization method is introduced to design the MIMO radar imaging measurement matrix, and the imaging effect is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of radar imaging technology, and in particular to a method and system for designing a MIMO imaging radar measurement matrix based on block sparse constraints. Background Art

[0002] Radar imaging of aerospace targets is useful for both civilian and military applications. Since the common inverse synthetic aperture radar (ISAR) imaging technology has complex motion compensation problems, it is difficult to apply it to the case of imaging maneuvering targets. Multiple-input multiple-output (MIMO) radar is a new type of radar structure that can be used for imaging; it is a radar in which multiple transmitters transmit multiple independent signals, and multiple receivers receive target echoes, and a larger virtual aperture is obtained after signal processing. Therefore, high resolution can be achieved even with a short coherent processing interval (CPI). In the literature Zhu, Y., Su, Y., Yu, W. (2010) An ISAR imaging method based on MIMO technique. IEEE Trans. Geosci. Remote Sens., 48 (8): 3290-3299, MIMO technology is used to improve the ISAR imaging results of short CPI. The literature Wang, D., Ma, X., Su, Y. (2010) Two-dimensional imaging via a narrowband MIMO radar system with two perpendicular linear arrays. IEEE Trans. Image Process., 19(5): 1269-1279. mentions that a MIMO radar can obtain a two-dimensional or three-dimensional (2D or 3D) image by transmitting only one pulse waveform. In these literatures, it is assumed that multiple waveforms are completely orthogonal.

[0003] However, in practical applications, it is difficult for waveforms of the same frequency to be completely orthogonal. The mutual interference of multiple waveforms will seriously reduce the quality of the target image. Krieger, G. (2014) MIMO-SAR: Opportunities and pitfalls. IEEE Trans. Geosci. Remote Sens., 52 (5): 2628-2645 and other literatures have proposed some new orthogonal waveforms, such as short-time shifted orthogonal waveforms and orthogonal frequency division multiplexing (OFDM) chirp waveforms, to improve imaging quality; however, further processing such as digital beamforming (DBF) is still required, otherwise the results are still unsatisfactory.

[0004] Compressed sensing (CS) is a new theory that uses the sparsity of signals to reconstruct signals using only partial sampling. Since radar targets are usually composed of several strong scatterers, the targets are sparse. In recent years, CS has been applied to MIMO radars to improve the imaging effect of MIMO radars. Generally speaking, CS consists of two parts: compressed measurement and sparse reconstruction. At present, almost all CS-MIMO imaging only considers "sparse reconstruction" but not "compressed measurement". Compressed measurement can reduce the amount of data for MIMO imaging. However, compressed measurement usually requires the addition of a measurement matrix to achieve it. Since the Gaussian random measurement matrix (GRMM) is incoherent with any basis matrix with a high probability, GRMM is usually used in CS recovery. Although GRMM usually satisfies mutual incoherence (MIP) and ensures the recovery of sparse signals, it is not necessarily the best matrix for sparse signals with special sparse structures. The literature Zelnik-Manor, L., Rosenblum, K., Eldar, YC (2010) Sensing Matrix Optimization for Block-Sparse Decoding. IEEE Trans. Signal Process., 59(9): 4300-4312 proposed using the weighted coherence minimization (WCM) method to design a block sparsity measurement matrix.

[0005] Therefore, it is urgent to design a multiple-input multiple-output (MIMO) imaging radar measurement matrix to achieve better imaging effects. Summary of the invention

[0006] The purpose of the present invention is to provide a MIMO imaging radar measurement matrix design method and system based on block sparsity constraints. On the basis of considering the block sparsity of the range image of the target, the MIMO imaging radar measurement matrix design is realized in combination with the weighted coherence minimization method, thereby improving the imaging effect.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A method for designing a MIMO imaging radar measurement matrix based on block sparse constraints comprises the following steps:

[0009] S1. Based on the MIMO radar imaging model, obtain the relevant parameters of the one-dimensional linear array in the MIMO radar, construct the transmission waveform matrix Φ, and determine the target echo in combination with the target range image;

[0010] S2, based on the block sparsity of the target range image Θ, the measurement matrix Ψ is designed;

[0011] S3, based on a compressed sensing method, using the measurement matrix Ψ to perform compression measurement on the target echo to obtain a compressed target echo;

[0012] S4, using a block sparse recovery algorithm to recover the target range image Θ from the compressed target echo Y′, and reconstructing a two-dimensional image using a sparse recovery algorithm.

[0013] Further, the S1, based on the MIMO radar imaging model, obtains relevant parameters of the one-dimensional linear array in the MIMO radar, constructs a transmission waveform matrix Φ, and determines the target echo in combination with the target range image, specifically including:

[0014] Construct a MIMO radar imaging model, assuming that the one-dimensional linear array is configured with M transmitting units and N receiving units; the distance between the transmitting units is Nd, and the distance between the receiving units is d; the transmission signal vector of the mth transmitting unit is The length is L; the imaging distance range is divided into K grids; the distance matrix corresponding to the kth distance unit is defined as

[0015] Then the transmission waveform matrix is ​​expressed as

[0016] in,

[0017] The transmit waveform matrix is ​​combined with the target range image to obtain the matrix form of the target echo, which is expressed as:

[0018] Y=ΦΘ (1)

[0019] In the formula, is the received signal matrix, y n is the received signal vector of the nth receiver unit; is the target range image sequence, where is the target distance image corresponding to the nth receiving unit, Where ρ k,mn is the complex scattering coefficient of the kth distance unit in the mth transmit and n receive channel.

[0020] Furthermore, the S2, based on the block sparsity of the target range image Θ, designs the measurement matrix Ψ, which specifically includes the following steps:

[0021] S2.1, through initialization settings, the measurement matrix design problem is expressed as:

[0022]

[0023] Where α∈(0,1) is a parameter for balancing the total coherence between blocks and the total coherence between sub-blocks; is the overall correlation between blocks; v t is the total sub-block correlation, where represents the mth diagonal element in G, m = 1, L, MK; η is the normalization penalty term;

[0024] S2.2, using the weighted coherence minimization method to solve the measurement matrix design problem, the measurement matrix Ψ = Ψ (γ+1) .

[0025] Furthermore, in S2.1, the measurement matrix design problem is expressed as The specific steps include:

[0026] Initialization setting: Consider G as a set of K×K sub-blocks, let G = (ΨΦ) * ΨΦ∈￡ MK×MK , according to the block structure of Θ, the size of each sub-block is M×M; let Ψ (0) =[I D ,0]Λ -0.5 U * , the number of iterations γ = 0, where I represents the identity matrix, the subscript D represents the dimension of the identity matrix, and I D Represents a D-dimensional identity matrix;

[0027] Define the overall dependency between blocks: Where G[i,j] represents the (i,j)th sub-block of G, i = 1, L, K; j = 1, L, K;

[0028] Define an overall sub-block dependency:

[0029] in, represents the mth diagonal element in G, m=1,L,MK;

[0030] Further define the normalized penalty term:

[0031] Therefore, the measurement matrix design problem is expressed as:

[0032] Among them, α∈(0,1) is a parameter to balance the total coherence between blocks and the total coherence between sub-blocks.

[0033] Furthermore, in S2.2, a weighted coherence minimization method is used to solve the measurement matrix design problem, and the measurement matrix Ψ=Ψ (γ+1) , specifically including the following steps:

[0034] make

[0035] In the formula,

[0036]

[0037]

[0038] represents the (a,b)th element of G[i,j];

[0039] Formula (4) can be further expressed as:

[0040]

[0041] in,

[0042] An iterative process is used to solve equation (5): In the γth iteration, the objective function is expressed as:

[0043]

[0044] Among them, G (γ) =Φ * Ψ (γ)* Ψ (γ) Φ,Ψ (γ) is the measurement matrix of the previous iteration;

[0045] The solution to the measurement matrix design problem is obtained:

[0046] Among them, UΛU * Yes * The eigenvalue decomposition of , and Δ D and V D They are Λ -0.5 U * Φh t (G (γ) )Φ * UΛ -0.5 The D largest eigenvalues ​​and their corresponding eigenvectors, where, according to the formula Calculate h t (G (γ) );

[0047] Cycle update G (γ) and (γ+1) Until the convergence condition is met, the final measurement matrix Ψ=Ψ (γ+1) .

[0048] Further, the step S3, based on a compressed sensing method, using the measurement matrix Ψ to perform compression measurement on the target echo to obtain a compressed target echo, specifically includes:

[0049] In the compressed measurement process of compressed sensing, a measurement matrix Ψ∈￡ is used D×(K+L) Compress the target echo, where D is the number of samples for compression measurement, and D < (K + L);

[0050] The compressed target echo is expressed as Y′=ΨY=ΨΦΘ.

[0051] The present invention also provides a MIMO imaging radar measurement matrix design system based on block sparse constraints, which is applied to the MIMO imaging radar measurement matrix design method based on block sparse constraints, and is characterized by comprising:

[0052] The target echo determination module is used to obtain the relevant parameters of the one-dimensional linear array in the MIMO radar to determine the target echo and construct the transmission waveform matrix;

[0053] A measurement matrix design module is used to design the measurement matrix based on the block sparsity of the target range image;

[0054] A compressed sensing module, used for performing compression measurement on the target echo using the measurement matrix based on a compressed sensing method to obtain a compressed target echo, and recovering a target range image from the compressed echo;

[0055] The image processing module is used to reconstruct a one-dimensional range image sequence based on the compressed target echo using a block sparse recovery algorithm and to reconstruct a two-dimensional image using a sparse recovery algorithm.

[0056] According to the specific embodiment provided by the present invention, the present invention discloses the following technical effects: the block sparsity-constrained MIMO imaging radar measurement matrix design method and system provided by the present invention considers the block sparsity of hrrp (High Resolution Radar Range Profile) and introduces the weighted coherence minimization method to design the MIMO radar imaging measurement matrix. Simulation results show that the designed measurement matrix has better imaging effect than GRMM and improves the imaging quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 It is a flowchart of a method for designing a MIMO imaging radar measurement matrix based on block sparse constraints in an embodiment of the present invention;

[0059] Figure 2 is the Gram matrix of the Gaussian random measurement matrix, where (2.1) and (2.2) are the global graph and the local graph respectively;

[0060] Figure 3 The Gram matrix diagram of the measurement matrix designed for the embodiment of the present invention, (3.1) and (3.2) are the overall diagram and the local diagram respectively;

[0061] Figure 4 is the imaging result of Gaussian random measurement matrix, where (4.1) is the range image sequence and (4.2) is the two-dimensional image;

[0062] Figure 5 The imaging result of the measurement matrix designed by the method described in the embodiment of the present invention, wherein (5.1) is a range image sequence, and (5.2) is a two-dimensional image;

[0063] Figure 6 Schematic diagram of a target model in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0065] The purpose of the present invention is to provide a MIMO imaging radar measurement matrix design method and system based on block sparsity constraints. On the basis of considering the block sparsity of the range image of the target, the MIMO imaging radar measurement matrix design is realized in combination with the weighted coherence minimization method, thereby improving the imaging effect.

[0066] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] like Figure 1 As shown, the MIMO imaging radar measurement matrix design method based on block sparse constraints provided by an embodiment of the present invention includes the following steps:

[0068] S1. Obtain relevant parameters of the one-dimensional linear array in the MIMO radar, construct the transmission waveform matrix Φ, and determine the target echo;

[0069] S2, based on the block sparsity of the target range image Θ, the measurement matrix Ψ is designed;

[0070] S3, based on a compressed sensing method, using the measurement matrix Ψ to perform compression measurement on the target echo to obtain a compressed target echo Y′;

[0071] S4, using a block sparse recovery algorithm to recover the target range image Θ from the compressed target echo Y′, and reconstructing a two-dimensional image using a sparse recovery algorithm.

[0072] In this embodiment, the S1, obtaining relevant parameters of a one-dimensional linear array in the MIMO radar, constructing a transmission waveform matrix Φ, and determining a target echo, specifically includes:

[0073] Assume that the one-dimensional linear array is configured with M transmitting units and N receiving units; the distance between the transmitting units is Nd, and the distance between the receiving units is d; the transmission signal vector of the mth transmitting unit is The length is L; the imaging distance range is divided into K grids; the distance matrix corresponding to the kth distance unit is defined as

[0074] Then the transmission waveform matrix is ​​expressed as in,

[0075] The matrix form of the target echo is obtained, which is expressed as: Y = ΦΘ; (1)

[0076] In the formula, is the received signal matrix, y n is the received signal vector of the nth receiver unit; is the target range image sequence, where is the target distance image corresponding to the nth receiving unit, Where ρ k,mn is the complex scattering coefficient of the kth distance unit in the mth transmit and n receive channel.

[0077] In this embodiment, in order to image the target, it is necessary to restore the target range image Θ from the compressed echo Y′. Θ has joint sparsity, that is, the positions of non-zero elements are different. There is no change between The non-zero elements in appear in blocks. kgn is the kth block, and the block length is the number of transmitted waveforms M. Therefore, Θ can be solved by the following optimization criterion

[0078]

[0079] where ε represents the noise level, ||·|| F,pis the mixing matrix norm:

[0080] In this embodiment, in order to accurately recover Θ, the mutual coherence between the columns of the matrix ΨΦ must be small enough. Usually, by minimizing the Frobenius norm To ensure small mutual coherence. When the transmission waveform matrix Φ is fixed, the measurement matrix Ψ can be designed by solving the minimization problem. However, if the block sparsity of Θ is not considered, the result Ψ may not be optimal and will not produce ideal results. The following considers the block sparsity of Θ to design the measurement matrix. Therefore, the S2, based on the block sparsity of the target range image Θ, designs the measurement matrix Ψ, which specifically includes the following steps:

[0081] S2.1, through initialization settings, the measurement matrix design problem is expressed as:

[0082]

[0083] This step specifically includes the following steps:

[0084] Initialization setting: According to the block structure of Θ, G can be regarded as a set of K×K sub-blocks, let According to the block structure of Θ, the size of each sub-block is M×M; let Ψ (0) =[I D ,0]Λ -0.5 U * , (where I represents the identity matrix and the subscript D represents the dimension of the identity matrix, so I D represents a D-dimensional identity matrix, and other identity matrices are defined similarly) The number of iterations γ = 0;

[0085] Define the overall dependency between blocks:

[0086] Where G[i,j] represents the (i,j)th sub-block of G, i = 1, L, K; j = 1, L, K;

[0087] Define an overall sub-block dependency:

[0088] in, represents the mth diagonal element in G, m=1,L,MK;

[0089] Further define the normalized penalty term:

[0090] Therefore, the measurement matrix design problem is expressed as:

[0091] Among them, α∈(0,1) is a parameter to balance the total coherence between blocks and the total coherence between sub-blocks.

[0092] S2.2, using the weighted coherence minimization method to solve the measurement matrix design problem, the measurement matrix Ψ = Ψ (γ+1) ; Specifically include:

[0093] make

[0094]

[0095] In the formula, represents the (a,b)th element of G[i,j];

[0096] Formula (4) can be further expressed as:

[0097]

[0098] in,

[0099]

[0100] That is, according to formula (5), h can be calculated η (G (γ) ), h μ (G (γ) ), h v (G (γ) );

[0101] An iterative process is used to solve equation (5): In the γth iteration, the objective function is expressed as:

[0102] Among them, G (γ) =Φ * Ψ (γ)* Ψ (γ) Φ,Ψ (γ) is the measurement matrix of the previous iteration;

[0103] The measurement matrix design problem is obtained Solution:

[0104]

[0105] Among them, UΛU * Yes * The eigenvalue decomposition of , and Δ D and V D They are Λ -0.5 U * Φh t (G (γ) )Φ * UΛ-0.5 The D largest eigenvalues ​​and their corresponding eigenvectors, where according to the calculation formula Calculate h t (G (γ) );

[0106] Cycle update G (γ) and (γ+1) Until the convergence condition is met, the final measurement matrix Ψ=Ψ (γ+1) .

[0107] In this embodiment, the S3, based on the compressed sensing method, uses the measurement matrix Ψ to perform compression measurement on the target echo to obtain the compressed target echo, specifically includes:

[0108] In the compressed measurement process of compressed sensing, a measurement matrix is ​​used Compress the target echo, where D is the number of samples for compression measurement, and D < (K + L);

[0109] The compressed target echo is expressed as Y′=ΨY=ΨΦΘ.

[0110] The simulation analysis and results are as follows:

[0111] In the simulation analysis, a 4-transmit 20-receive linear array was used. The array element spacing between the receiving array element and the transmitting array element is 3m and 60m respectively. The transmitted signal is a polyphase coded signal with a code length of 450, a bandwidth of 600MHz, and a center frequency of 10GHz. Figure 6 The same target model is 5 km away from the center of the array. The imaging scene range is set to 50 m, and the length of the received target echo is 650. The echo is compressed using a measurement matrix of size 520×650. The number of iterations of the measurement matrix optimization algorithm is set to 20, and the algorithm parameter α=0.9 is used to design the measurement matrix. A Gaussian random measurement matrix is ​​used for comparison. Figure 2 and Figure 3 The Gram matrices (diagonal elements are set to zero) of the Gaussian random measurement matrix and the measurement matrix designed by the proposed method are shown respectively. * ΨΦ. For better clarity, the part of the Gram matrix from row 0 to column 50 is also enlarged. It can be seen that the designed measurement matrix has a lower total sub-block coherence because this method sets α = 0.9. The Gaussian random measurement matrix does not weight the block coherence.

[0112] The JB-SL0 algorithm (joint-blocksmoothedL0, JB-SL0, joint block smoothed L0 algorithm) is used to reconstruct the target's range image, and then the SL0 algorithm is used to obtain a two-dimensional image. The imaging results of the Gaussian random measurement matrix and the measurement matrix designed by this method are shown in Figure 2. Figure 4 and Figure 5 shown. Figure 4 The image contrast (IC) and image entropy (IE) of (4.2) are IC = 8.6302 and IE = 6.9034 respectively. And Figure 5 The image quality index of (4.2) is IC = 8.8685, IE = 6.8853. The larger the IC value and the lower the IE value, the better the image quality. Therefore, compared with the Gaussian random measurement matrix, the measurement matrix designed by this method has a better imaging effect.

[0113] Example 2

[0114] The embodiment of the present invention provides a MIMO imaging radar measurement matrix design system based on block sparse constraints, which is applied to the MIMO imaging radar measurement matrix design method based on block sparse constraints described in Example 1, including:

[0115] The target echo determination module is used to obtain the relevant parameters of the one-dimensional linear array in the MIMO radar to determine the target echo and construct the transmission waveform matrix;

[0116] A measurement matrix design module is used to design the measurement matrix based on the block sparsity of the target range image;

[0117] A compressed sensing module, used for performing compression measurement on the target echo using the measurement matrix based on a compressed sensing method to obtain a compressed target echo, and recovering a target range image from the compressed echo;

[0118] The image processing module is used to reconstruct a one-dimensional range image sequence based on the compressed target echo using a block sparse recovery algorithm and to reconstruct a two-dimensional image using a sparse recovery algorithm.

[0119] The remaining technical features in this embodiment can be flexibly selected by those skilled in the art according to actual conditions to meet different specific practical needs. However, it is obvious to those skilled in the art that it is not necessary to adopt these specific details to implement the present invention. In other examples, in order to avoid confusing the present invention, the well-known components, structures or parts are not specifically described, which are all within the technical protection scope defined by the technical solution claimed for protection in the claims of the present invention.

[0120] In the description of the present invention, unless otherwise clearly specified and limited, the terms "set", "install", "connect", "connect" and the like are all in a broad sense and should be understood in a broad sense by those skilled in the art. For example, it can be a fixed connection or a movable connection, or an integral connection or a partial connection, a mechanical connection or an electrical connection, a direct connection or an indirect connection through an intermediate medium, or a connection between two components, etc. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances, that is, the expression of the text language and the implementation of the actual technology can correspond flexibly, and the expression of the text language (including the drawings) of the specification of the present invention does not constitute any single restrictive interpretation of the claims.

[0121] Modifications and changes made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the scope of protection of the claims attached to the present invention. In the above description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details are not necessary to practice the present invention. In other examples, in order to avoid confusing the present invention, well-known technologies, such as specific construction details, operating conditions and other technical conditions, are not specifically described.

[0122] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A MIMO imaging radar measurement matrix design method based on block sparse constraints, characterized in that: The following steps are involved: S1. Based on the MIMO radar imaging model, obtain the relevant parameters of the one-dimensional linear array in the MIMO radar, construct the transmission waveform matrix Φ, and determine the target echo in combination with the target range image; S2, based on the block sparsity of the target range image Θ, the measurement matrix Ψ is designed; S3, based on a compressed sensing method, using the measurement matrix Ψ to perform compression measurement on the target echo to obtain a compressed target echo Y′; S4, using a block sparse recovery algorithm to recover the target range image Θ from the compressed target echo Y′, and reconstructing a two-dimensional image using a sparse recovery algorithm.

2. The method for designing a MIMO imaging radar measurement matrix based on block sparse constraints according to claim 1, characterized in that: In S1, based on the MIMO radar imaging model, relevant parameters of the one-dimensional linear array in the MIMO radar are obtained, a transmission waveform matrix Φ is constructed, and the target echo is determined in combination with the target range image, which specifically includes: Construct a MIMO radar imaging model, assuming that the one-dimensional linear array is configured with M transmitting units and N receiving units; the distance between the transmitting units is Nd, and the distance between the receiving units is d; the transmission signal vector of the mth transmitting unit is The length is L; the imaging distance range is divided into K grids; the distance matrix corresponding to the kth distance unit is defined as Then the transmission waveform matrix is ​​expressed as in, The transmit waveform matrix is ​​combined with the target range image to obtain the matrix form of the target echo, which is expressed as: Y=ΦΘ (1) In the formula, is the received signal matrix, y n is the received signal vector of the nth receiver unit; is the target range image sequence, where is the target distance image corresponding to the nth receiving unit, Where ρ k,mn is the complex scattering coefficient of the kth distance unit in the mth transmit and n receive channel.

3. The method for designing a MIMO imaging radar measurement matrix based on block sparse constraints according to claim 1, characterized in that: The S2, based on the block sparsity of the target range image Θ, designs the measurement matrix Ψ, which specifically includes the following steps: S2.1, through initialization settings, the measurement matrix design problem is expressed as: Where α∈(0,1) is a parameter for balancing the total coherence between blocks and the total coherence between sub-blocks; is the overall correlation between blocks; v t is the total sub-block correlation, where represents the mth diagonal element in G, m = 1, L, MK; η is the normalization penalty term; S2.2, using the weighted coherence minimization method to solve the measurement matrix design problem, the measurement matrix Ψ = Ψ (γ+1) .

4. The method for designing a MIMO imaging radar measurement matrix based on block sparse constraints according to claim 1, characterized in that: In S2.1, by initialization setting, the measurement matrix design problem is expressed as The specific steps include: Initialization setting: Consider G as a set of K×K sub-blocks, let G = (ΨΦ) * ΨΦ∈￡ MK×MK , according to the block structure of Θ, the size of each sub-block is M×M; let Ψ (0) =[I D ,0]Λ -0.5 U * , the number of iterations γ = 0, where I represents the identity matrix, the subscript D represents the dimension of the identity matrix, and I D Represents a D-dimensional identity matrix; Define the overall correlation between blocks: Where G[i,j] represents the (i,j)th sub-block of G, i = 1, L, K; j = 1, L, K; Define an overall sub-block dependency: in, represents the mth diagonal element in G, m=1,L,MK; Further define the normalized penalty term: Therefore, the measurement matrix design problem is expressed as: Among them, α∈(0,1) is a parameter to balance the total coherence between blocks and the total coherence between sub-blocks.

5. The method for designing a MIMO imaging radar measurement matrix based on block sparse constraints according to claim 4, characterized in that: S2.2, using the weighted coherence minimization method to solve the measurement matrix design problem, to obtain the measurement matrix Ψ=Ψ (γ+1) , specifically including the following steps: make In the formula, represents the (a,b)th element of G[i,j]; Formula (4) can be further expressed as: in, An iterative process is used to solve equation (5): In the γth iteration, the objective function is expressed as: Among them, G (γ) =Φ * Ψ (γ)* Ψ (γ) Φ,Ψ (γ) is the measurement matrix of the previous iteration; The solution to the measurement matrix design problem is obtained: Among them, UΛU * Yes * The eigenvalue decomposition of , and Δ D and V D They are Λ -0.5 U * Φh t (G (γ) )Φ * UΛ -0.5 The D largest eigenvalues ​​and their corresponding eigenvectors, where, according to the formula Calculate h t (G (γ) ); Cycle update G (γ) and (γ+1) Until the convergence condition is met, the final measurement matrix Ψ=Ψ (γ+1) .

6. The method for designing a MIMO imaging radar measurement matrix based on block sparse constraints according to claim 1, characterized in that: The S3, based on the compressed sensing method, using the measurement matrix Ψ to perform compressive measurement on the target echo to obtain a compressed target echo, specifically includes: In the compressed measurement process of compressed sensing, a measurement matrix Ψ∈￡ is used D×(K+L) Compress the target echo, where D is the number of samples for compression measurement, and D < (K + L); The compressed target echo is expressed as Y′=ΨY=ΨΦΘ.

7. A MIMO imaging radar measurement matrix design system based on block sparse constraints, applied to the MIMO imaging radar measurement matrix design method based on block sparse constraints according to any one of claims 1 to 6, characterized in that: include: The target echo determination module is used to obtain the relevant parameters of the one-dimensional linear array in the MIMO radar to determine the target echo and construct the transmission waveform matrix; A measurement matrix design module is used to design the measurement matrix based on the block sparsity of the target range image; A compressed sensing module, used for performing compression measurement on the target echo using the measurement matrix based on a compressed sensing method to obtain a compressed target echo, and recovering a target range image from the compressed echo; The image processing module is used to reconstruct a one-dimensional range image sequence based on the compressed target echo using a block sparse recovery algorithm and to reconstruct a two-dimensional image using a sparse recovery algorithm.

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