MIMO multi-view radar correlated imaging method and device based on image entropy and medium

Abstract

The invention discloses an image entropy-based MIMO (Multiple Input Multiple Output) multi-view radar correlated imaging method and device and a medium, and aims to solve the new problem of correlation mismatch between radar echoes and reference signals of corresponding imaging resolution units caused by uncertain fluctuation of scattering intensity of the resolution units along with view angles in radar correlated imaging. The method comprises the following steps: starting from suppressing the energy distribution of an imaging resolution unit, based on imaging scene sparse reconstruction, adding image entropy to construct a new optimization model, and solving the optimization model by adopting a soft threshold iterative shrinkage algorithm to obtain a high-resolution multi-view MIMO radar correlated imaging result. The number of imaging channels is increased, the observation visual angle is enlarged, the randomness of a radiation field is improved, and meanwhile high-precision imaging can be achieved under the condition that the scattering intensity of the resolution unit fluctuates.

Description

Technical Field

[0001] The present invention belongs to the field of radar correlation imaging signal processing, and in particular relates to a MIMO multi-view radar correlation imaging method, device and medium based on image entropy. Background Art

[0002] Radar correlation imaging originated from optical ghost imaging and is a new type of real aperture imaging radar. The transmitter emits multiple temporally and spatially unrelated random modulation signals to form a two-dimensional random radiation field in the imaging area. The image is reconstructed by correlating the echo signal with the deduced reference signal matrix. Compared with traditional real aperture radar imaging, the imaging resolution is related to the randomness of the radiation field and no longer requires a large antenna array. At the same time, it is no longer limited by relative rotation with the target, overcoming the defects of relative motion imaging radar that relies on motion and is constrained by motion. Radar correlation imaging provides new ideas and directions for solving the defects of traditional radar imaging and has attracted widespread attention from scholars at home and abroad.

[0003] MIMO (multiple input multiple output) multi-view radar correlation imaging increases the resolution by increasing the receiving channels and expanding the observation angle. However, the fluctuation of the target scattering coefficient with the change of the angle of view will also interfere with radar correlation imaging. Existing traditional radar imaging algorithms and radar correlation imaging are mostly based on isotropic ideal point target models. However, radar correlation imaging is heavily dependent on the correlation between the radar echo and the reference signal of the corresponding imaging resolution unit. Therefore, the fluctuation of the resolution unit scattering intensity with the uncertainty of the angle of view in MIMO or MISO (Multiple Input Single Output) radar correlation imaging will cause the correlation mismatch between the radar echo and the reference signal of the corresponding imaging resolution unit, causing the energy dispersion of the imaging resolution unit, which seriously affects the quality of radar correlation imaging.

[0004] The radar image of a complex target is an estimate of the target scattering distribution function obtained by weighting and coherent integration of the target broadband scattering measurement data. Some scholars have introduced the basic concepts of target scattering function and scattering distribution function that are consistent with the definition of traditional radar cross section (RCS), combined with the scattering mechanism of classical targets and radar image analysis, to discuss the understanding of high-resolution radar images of complex targets and the interpretation of pixel values. It is pointed out that the pixel value of the radar image resolution unit should not be directly interpreted as the RCS level of the target. The scattering intensity of the radar imaging resolution unit is affected by the radar radiation frequency, target material, overall shape, and the position of the resolution unit in the target as a whole, showing uncertain fluctuations with the change of viewing angle. The fluctuation of the scattering intensity of the radar image resolution unit with the viewing angle is different from the fluctuation of the overall RCS of the target with the viewing angle, and it cannot be described by the Swerling fluctuation model of the overall RCS of the target in the time dimension. For this reason, we choose to use non-uniform distribution to characterize the fluctuation of the scattering intensity of the radar image resolution unit with the change of viewing angle.

[0005] At present, scholars have proposed a sparse total variation regularization algorithm to address the impact of fluctuations in the scattering intensity of resolution units on radar correlation imaging. In the sparse reconstruction model, a total variation term is added to constrain the fluctuation energy. Although this method can suppress the fluctuation energy to a certain extent and improve the visual effect of the radar image, the target resolution unit often has local "defocusing" problems.

[0006] In view of the shortcomings of existing research, this paper proposes a MIMO multi-view radar correlation imaging algorithm based on image entropy. The algorithm adopts a "result-oriented" strategy, starting from suppressing the energy dispersion of the imaging resolution unit, based on the sparse reconstruction of the imaging scene, adding image entropy to construct a new optimization model, and using the soft threshold iterative shrinkage algorithm (ISTA) method to solve the optimization model, and obtain high-resolution multi-view MIMO radar correlation imaging results. Simulation results show that the present invention can achieve high-precision imaging in the case of fluctuations in the scattering intensity of the resolution unit. Summary of the invention

[0007] Purpose of the invention: To address the problem of deterioration in imaging quality due to fluctuations in scattering intensity of target area resolution units in MIMO multi-view radar correlation imaging, a MIMO multi-view radar correlation imaging method, device and medium based on image entropy are proposed.

[0008] Technical solution: The MIMO multi-view radar correlation imaging method based on image entropy described in the present invention specifically includes the following steps:

[0009] (1) The number of transmitting array elements of the MIMO radar is M, and the number of receiving array elements is N. A set of temporally and spatially unrelated random modulation signals is transmitted at the transmitting end to divide the imaging plane into L imaging units. According to the positions of the transmitting and receiving array elements and the imaging unit positions, N reference matrices are obtained. The N reference matrices are spliced ​​in the time dimension to obtain the expanded-dimensional reference matrix A.

[0010] (2) A Gaussian mixture distribution model is used to characterize the fluctuation of the scattering intensity of the radar image resolution unit with the change of viewing angle; N receiving array elements receive N echo signals, and the N echo signals are spliced ​​in the time dimension to obtain the expanded-dimensional echo signal y;

[0011] (3) Under the premise of sparse targets, a MIMO multi-view radar correlation imaging model based on image entropy is constructed, the number of iterations k is set to 0, and the initial target scattering sparse vector σ 0 =0;

[0012] (4) According to the MIMO multi-view radar correlation imaging model based on image entropy, the expanded reference matrix A and the expanded echo signal y are jointly processed and solved by the soft threshold iterative shrinkage algorithm ISTA. In the (k+1)th iteration, the solution σ based on the kth iteration is k , get the scattering coefficient value σ for multi-view targets k+1 estimates;

[0013] (5) Set the maximum number of iterations K max And the convergence threshold η, let k = k + 1, and determine whether the number of iterations is reached or the convergence condition is met If satisfied, the comprehensive value of the MIMO multi-view radar correlation imaging scattering coefficient is obtained; if not satisfied, return to step (4);

[0014] (6) The obtained target scattering coefficient vector σ is converted back into a scattering coefficient matrix of a two-dimensional imaging area resolution unit to realize MIMO multi-view radar correlation imaging.

[0015] Furthermore, the implementation process of step (1) is as follows:

[0016] The signal transmitted by the mth transmitting array element is St m (t), then the reference signal of the nth receiving array element and the mth transmitting array element with respect to the lth grid is:

[0017]

[0018] Among them, R m and R n are the position vectors of the mth transmitting array element and the nth receiving array element, r lis the position vector of the lth grid center, c is the speed of light, and t represents time; the echo signal received by the nth receiving element from the mth transmitting element is recorded as:

[0019]

[0020] Among them, w m,n (t) is the noise signal of the channel; σ m,n,l For the scattering coefficient corresponding to the center of the lth resolution unit under this viewing angle, the time is discretized, the time sampling number is J, the echo signal and reference signal received by the nth receiving array element from the mth transmitting array element are discretized in time dimension, and the MIMO multi-view radar correlation imaging equation is written as:

[0021] y m,n =A m,n σ m,n +w m,n

[0022]

[0023] Among them, y m,n A is the echo vector received by the nth receiving element from the mth transmitting element; m,n is the reference matrix derived from this perspective; σ m,n =[σ m,n,1 σ m,n,2 …σ m,n,L ] T is the scattering coefficient vector of the viewing angle; w m,n is the noise vector;

[0024] According to the position of the transmitting and receiving array elements and the position of the imaging unit, N reference matrices A1, A2, ..., A are obtained. N , concatenate the N reference matrices in the time dimension to obtain an expanded reference matrix A = [A1, A2, ..., A N ] T .

[0025] Furthermore, the implementation process of step (2) is as follows:

[0026] The scattering intensity vector σ of a resolution unit at different viewing angles l =[σ l,1 … σ l,M×N ] obeys the Gaussian mixture distribution model, and its probability density function expression is as follows:

[0027]

[0028] Where K represents the number of Gaussian components; μ i ,Σ iRepresents the mean and covariance matrix of each Gaussian component, π i Represents the weight coefficient of each Gaussian component;

[0029] For the nth receiving array element, the echo signal it receives comes from all M transmitting array elements:

[0030]

[0031] Among them, σ n is the comprehensive imaging result of the nth receiving array element; Δσ m,n =σ m,n -σ n It means that for the nth receiving channel, the imaging result corresponding to the mth transmitting channel is equal to σ n The difference between A and B is the fluctuation term of the viewing angle. n is the reference matrix corresponding to the nth receiving array element;

[0032] The MIMO system is adopted to obtain the echo data of N receiving channels. The echo data of multiple receiving channels are spliced ​​in the time dimension by using the data layer expansion fusion method. The randomness of the radiation field reference signal is improved by expanding the spatial dimension. The expanded dimensional imaging equation based on the fluctuation of the resolution unit scattering intensity is derived as follows:

[0033]

[0034] remember

[0035]

[0036] Where y is the expanded-dimensional echo vector of MIMO radar correlation imaging; A n is the reference matrix derived from the nth receiving array element, diag(A) is the matrix of A1, A2, …, A N is a block diagonal matrix with diagonal elements; For is a block diagonal matrix with diagonal elements; Δσ n =σ n -σ is the imaging result σ corresponding to the nth receiving channel n The difference between the comprehensive inversion imaging result σ of the MIMO system.

[0037] Furthermore, the implementation process of step (3) is as follows:

[0038] In the correlation imaging model, find a solution that makes the number of non-zero elements in the scattering coefficient matrix as small as possible, then the objective function can be written as the minimization of the l0 norm of σ. Replace the objective function in the optimization problem with the l1 norm for solution, and the model is transformed into:

[0039]

[0040] In the formula, The l1-norm optimization method is a linear convex problem;

[0041] The new definition of image entropy in the sparse reconstruction system is adopted, and the probability defined by the gray value frequency in the traditional image entropy is replaced by the probability defined by l p Entropy defined in norm form; for a radar imaging space consisting of L imaging grids, the scattering coefficient of the lth grid is σ l , the corresponding probability is get

[0042] By writing the image entropy into the objective function, the imaging model of MIMO radar correlation imaging under the fluctuation of target resolution unit scattering intensity is obtained:

[0043]

[0044] Select λ1 and λ2, λ2 = μλ1, and express the above formula in a more general way to obtain the target expression:

[0045]

[0046] Furthermore, the implementation process of step (4) is as follows:

[0047] set up In the (k+1)th iteration, based on the solution σ of the kth iteration k , using the quadratic approximation:

[0048]

[0049] Where t = (2λ max (A H A)) -1 ; After adding l1 norm and image entropy constraints, the optimization model is:

[0050]

[0051] make For h(|σ l |) Do a first-order Taylor expansion at: According to the principle of ISTA, the final iterative formula is obtained:

[0052]

[0053] A device according to the present invention includes a memory and a processor, wherein:

[0054] A memory for storing computer programs that can be run on the processor;

[0055] The processor is used to execute the steps of the MIMO multi-view radar correlation imaging method based on image entropy as described above when running the computer program.

[0056] A storage medium described in the present invention stores a computer program, and when the computer program is executed by at least one processor, the steps of the MIMO multi-view radar correlation imaging method based on image entropy as described above are implemented.

[0057] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: The present invention adopts a MIMO radar correlation imaging system, which increases the number of imaging channels, expands the observation angle, increases the randomness of the radiation field, and can more comprehensively represent complex targets compared with the MISO correlation imaging system; the present invention starts from suppressing the energy dispersion of the imaging resolution unit, based on the sparse reconstruction of the imaging scene, adding image entropy to construct a new optimization model, and uses ISTA to solve the optimization model, which can reduce the influence of the scattering intensity fluctuation of the target resolution unit on the imaging quality, and obtain a high-resolution multi-angle MIMO radar correlation imaging result. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a flow chart of the MIMO multi-view radar correlation imaging method based on image entropy;

[0059] Figure 2 It is a schematic diagram of the MIMO radar correlation imaging principle of the present invention;

[0060] Figure 3 It is the imaging effect diagram of each method when the scattering intensity of the target resolution unit does not fluctuate;

[0061] Figure 4 It is a diagram of the imaging effect of each method when the scattering intensity fluctuation of the target resolution unit changes with the receiving viewing angle and satisfies the Gaussian mixture distribution of mean and variance. DETAILED DESCRIPTION

[0062] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0063] like Figure 1 As shown, the present invention proposes a MIMO multi-view radar correlation imaging method based on image entropy. On the basis of the l1 norm sparse reconstruction model, image entropy is added to construct a new optimization model, and ISTA is used to solve the optimization model to obtain a high-resolution multi-view MIMO radar correlation imaging result. The specific implementation process is as follows:

[0064] Step 1: Figure 2As shown in the figure, the number of transmitting array elements of the MIMO radar is M, the number of receiving array elements is N, and a set of temporally and spatially unrelated random modulation signals is transmitted at the transmitting end to divide the imaging plane into L imaging units. Then the reference signal of the nth receiving array element and the mth transmitting array element with respect to the lth grid can be written as:

[0065]

[0066] Among them, St m (t) is the signal transmitted by the mth transmitting array element, R m and R n are the position vectors of the mth transmitting array element and the nth receiving array element, r l is the position vector of the lth grid center, c is the speed of light, and t represents time. The echo signal received by the nth receiving element from the mth transmitting element is recorded as:

[0067]

[0068] Among them, w m,n (t) is the noise signal of the channel; σ m,n,l For the scattering coefficient corresponding to the center of the nth resolution unit under this perspective, the time is discretized, the time sampling number is J, and the echo signal and reference signal received by the nth receiving element from the mth transmitting element are discretized in time dimension. The MIMO multi-perspective radar correlation imaging equation can be written as:

[0069] y m,n =A m,n σ m,n +w m,n

[0070]

[0071] Among them, y m,n A is the echo vector received by the nth receiving element from the mth transmitting element; m,n is the reference matrix derived from this perspective; σ m,n =[σ m,n,1 σ m,n,2 …σ m,n,L ] T is the scattering coefficient vector of the viewing angle; w m,n is the noise vector.

[0072] According to the position of the transmitting and receiving array elements and the position of the imaging unit, N reference matrices A1, A2, ..., A N , concatenate the N reference matrices in the time dimension to obtain an expanded reference matrix A = [A1, A2, ..., A N ] T .

[0073] Step 2: Use the Gaussian mixture distribution model to characterize the fluctuation of the scattering intensity of the radar image resolution unit with the change of viewing angle, and calculate the expanded-dimensional echo signal y.

[0074] The radar image of a complex target is an estimate of the target scattering distribution function obtained by weighting and coherent integration of the target broadband scattering measurement data. Some scholars have introduced the target scattering function and the basic concepts of the scattering distribution function that are consistent with the traditional RCS definition, combined with the scattering mechanism of the classic target and radar image analysis, to discuss the understanding of high-resolution radar images of complex targets and the interpretation of pixel values. Some scholars pointed out that the pixel value of the radar image resolution unit should not be directly interpreted as the RCS level of the target. The scattering intensity of the radar imaging resolution unit is affected by the radar radiation frequency, the target material, the overall shape, and the position of the resolution unit in the target as a whole, showing uncertain fluctuations with the change of viewing angle. The fluctuation of the scattering intensity of the radar image resolution unit with the viewing angle is different from the fluctuation of the overall RCS of the target with the viewing angle, and it cannot be described by the Swerling fluctuation model of the overall RCS of the target in the time dimension. The fluctuation of the scattering intensity of the resolution unit is a non-cooperative error with strong uncertainty. For this reason, the Gaussian mixture distribution model is chosen to characterize the fluctuation of the scattering intensity of the radar image resolution unit with the change of viewing angle. Assume that the scattering intensity vector σ of a certain resolution unit at different viewing angles l =[σ l,1 … σ l,M×N ] obeys the Gaussian mixture distribution model, and its probability density function expression is as follows:

[0075]

[0076] Where K represents the number of Gaussian components; μ i ,∑ i Represents the mean and covariance matrix of each Gaussian component, which are experimental setting values. In order to facilitate subsequent experiments, let in is the diagonal element of the covariance matrix of each Gaussian component; π i Represents the weight coefficient of each Gaussian component and satisfies The Gaussian mixture distribution samples according to weights, usually based on the weights of the various Gaussian components to determine which component is selected as the distribution of the sampled data. The Gaussian component with a larger weight has a higher probability of being selected, which ensures that the sample is more likely to come from the Gaussian component with a larger weight, thus conforming to the distribution characteristics of the Gaussian mixture distribution model.

[0077] For the nth receiving array element, the echo signal it receives comes from all M transmitting array elements:

[0078]

[0079] Among them, σ n is the comprehensive imaging result of the nth receiving array element; Δσ m,n =σ m,n -σ n It means that for the nth receiving channel, the imaging result corresponding to the mth transmitting channel is equal to σ n The difference between A and B is the fluctuation term of the viewing angle. n is the reference matrix corresponding to the nth receiving array element.

[0080] By adopting the MIMO system, the echo data of N receiving channels are obtained. The echo data of multiple receiving channels can be spliced ​​in the time dimension by using the data layer expansion fusion method. The randomness of the radiation field reference signal is improved by expanding the spatial dimension. The expanded dimensional imaging equation based on the fluctuation of the resolution unit scattering intensity is derived as follows:

[0081]

[0082] remember

[0083]

[0084] Where y is the expanded-dimensional echo vector of MIMO radar correlation imaging; A n is the reference matrix derived from the nth receiving array element, diag(A) is the matrix of A1, A2, …, A N is a block diagonal matrix with diagonal elements; For is a block diagonal matrix with diagonal elements; Δσ n =σ n -σ is the imaging result σ corresponding to the nth receiving channel n The difference between the comprehensive inversion imaging result σ of the MIMO system.

[0085] Step 3: Under the premise of sparse targets, a MIMO multi-view radar correlation imaging model based on image entropy is constructed, and the number of iterations k is set to 0, and the initial target scattering sparse vector σ 0 =0.

[0086] The law of large numbers shows that the more samples there are, the more stable the statistical characteristics are. In order to ensure the quality of the associated imaging when the scattering intensity of the resolution unit fluctuates, it is expected to control the energy dispersion in a statistical sense, concentrate the energy on the target resolution unit, and suppress the energy dissipation caused by fluctuations.

[0087] In the correlation imaging model, it is assumed that the target is sparse. Among all solutions, we need to find a solution that minimizes the number of non-zero elements in the scattering coefficient matrix. The objective function can be written as the minimization of the l0 norm of σ. The classic algorithm for this model is the matching pursuit algorithm. In the actual solution process, the l0 norm is not a convex optimization problem and is not easy to solve. However, the reference signal matrix columns in correlation imaging are uncorrelated and easy to meet the RIP criterion. We can replace the objective function in the optimization problem with the l1 norm for solution. At this time, the model is transformed into:

[0088]

[0089] In the formula The above l1-norm optimization method is a linear convex problem that can be solved efficiently using many available algorithms.

[0090] Image entropy is expressed as the bit average of the image grayscale set, which can show the aggregation characteristics of the image grayscale distribution. The smaller the image entropy, the more concentrated the grayscale distribution is on individual grayscale values. Therefore, we hope to control the energy distribution of the radar image by minimizing the image entropy so that the energy is concentrated on the target resolution unit.

[0091] In the actual imaging process of radar correlation imaging, the scattering coefficient of each grid is different, and unlike the grayscale image, which has a clear grayscale value range, if the traditional image entropy definition method is adopted, regardless of whether there is fluctuation, the image entropy obtained each time is close to logL. This invention adopts a new definition method of image entropy under the sparse reconstruction system, replacing the probability defined by the grayscale value occurrence frequency in the traditional image entropy with the probability defined by l p For a radar imaging space consisting of L imaging grids, the scattering coefficient of the lth grid is σ l , the corresponding probability is get

[0092] By writing the image entropy into the objective function, the imaging model of MIMO radar correlation imaging under the fluctuation of target resolution unit scattering intensity is obtained:

[0093]

[0094] By choosing appropriate λ1 and λ2 (λ2 = μλ1), the above formula can be represented in a more general way to obtain the target expression:

[0095]

[0096] Step 4: According to the MIMO multi-view radar correlation imaging model based on image entropy, the expanded reference matrix A and the expanded echo signal y are jointly processed. The ISTA method is used. In the k+1th iteration, the solution σ based on the kth iteration is obtained. k , get the mean scattering coefficient σ for multi-view targets k+1 Estimates.

[0097] The present invention adopts the soft threshold iterative shrinkage (ISTA) algorithm to solve the above equation. The algorithm was originally proposed to solve the l1 norm model. In the k+1th iteration, based on the solution σ of the kth iteration k , using the quadratic approximation:

[0098]

[0099] Where t = (2λ max (A H A)) -1 After adding the l1 norm and image entropy constraints, the optimization model can be written as:

[0100]

[0101] make For h(|σ l |) Do a first-order Taylor expansion at:

[0102] make

[0103] When g(σ l )When the partial derivative is 0, the function takes the minimum value. When the partial derivative is 0, σ l The value of, that is:

[0104]

[0105] Can get

[0106] when hour, when hour, at this time and With the same module, exchanging the independent and dependent variables, we can get:

[0107]

[0108] make have:

[0109]

[0110] Step 5: Set the maximum number of iterations K max And a suitable convergence threshold η (a normal quantity close to zero, generally set to less than 10^(-5) in experiments), let k = k+1, and determine whether the number of iterations has been reached or the convergence condition has been met If it is satisfied, the comprehensive value of the MIMO multi-view radar correlation imaging scattering coefficient is obtained; if it is not satisfied, return to step 4.

[0111] Step 6: Use the reshape function in MATLAB to convert the target scattering coefficient vector σ into a two-dimensional imaging area resolution unit scattering coefficient matrix, and display it through the imagesc function.

[0112] The radar image reconstructed by the present invention is compared with the images reconstructed by traditional methods (correlation method, violation method, stage singular value decomposition (TSVD), total variation regularization algorithm, orthogonal matching pursuit algorithm (OMP), l1 norm sparse reconstruction method, sparse total variation radar correlation imaging algorithm), and the specific results are as follows: Figure 3 , Figure 4 As shown, it can be seen that when the fluctuation of the resolution unit scattering intensity is not considered, all algorithms except the correlation method can achieve relatively accurate reconstruction. When the resolution unit scattering intensity fluctuates sharply with the receiving angle of view, the other algorithms will have different degrees of "blurring", while the algorithm proposed in the present invention can still achieve accurate reconstruction, which shows that the algorithm proposed in the present invention is effective.

[0113] The present invention also provides an apparatus, comprising a memory and a processor, wherein: the memory is used to store a computer program that can be run on the processor; the processor is used to execute the steps of the MIMO multi-view radar correlation imaging method based on image entropy as described above when running the computer program.

[0114] The present invention also provides a storage medium having a computer program stored thereon. When the computer program is executed by at least one processor, the steps of the MIMO multi-view radar correlation imaging method based on image entropy as described above are implemented.

[0115] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A MIMO multi-view radar correlation imaging method based on image entropy, characterized in that: The following steps are involved: (1) The number of transmitting array elements of the MIMO radar is M, and the number of receiving array elements is N. A set of temporally and spatially unrelated random modulation signals is transmitted at the transmitting end to divide the imaging plane into L imaging units. According to the positions of the transmitting and receiving array elements and the imaging unit positions, N reference matrices are obtained. The N reference matrices are spliced ​​in the time dimension to obtain the expanded-dimensional reference matrix A. (2) A Gaussian mixture distribution model is used to characterize the fluctuation of the scattering intensity of the radar image resolution unit with the change of viewing angle; N receiving array elements receive N echo signals, and the N echo signals are spliced ​​in the time dimension to obtain the expanded-dimensional echo signal y; (3) Under the premise of sparse targets, a MIMO multi-view radar correlation imaging model based on image entropy is constructed, the number of iterations k is set to 0, and the initial target scattering sparse vector σ 0 =0; (4) According to the MIMO multi-view radar correlation imaging model based on image entropy, the expanded reference matrix A and the expanded echo signal y are jointly processed and solved by the soft threshold iterative shrinkage algorithm ISTA. In the (k+1)th iteration, the solution σ based on the kth iteration is k , get the scattering coefficient value σ for multi-view targets k+1 estimates; (5) Set the maximum number of iterations K max And the convergence threshold η, let k = k + 1, and determine whether the number of iterations is reached or the convergence condition is met If satisfied, the comprehensive value of the MIMO multi-view radar correlation imaging scattering coefficient is obtained; if not satisfied, return to step (4); (6) The obtained target scattering coefficient vector σ is converted back into a scattering coefficient matrix of a two-dimensional imaging area resolution unit to realize MIMO multi-view radar correlation imaging.

2. The MIMO multi-view radar correlation imaging method based on image entropy according to claim 1 is characterized in that: The implementation process of step (1) is as follows: The signal transmitted by the mth transmitting array element is St m (t), then the reference signal of the nth receiving array element and the mth transmitting array element with respect to the lth grid is: Among them, R m and R n are the position vectors of the mth transmitting array element and the nth receiving array element, r l is the position vector of the lth grid center, c is the speed of light, and t represents time; the echo signal received by the nth receiving element from the mth transmitting element is recorded as: Among them, w m,n (t) is the noise signal of the channel; σ m,n,l For the scattering coefficient corresponding to the center of the lth resolution unit under this viewing angle, the time is discretized, the time sampling number is J, the echo signal and reference signal received by the nth receiving array element from the mth transmitting array element are discretized in time dimension, and the MIMO multi-view radar correlation imaging equation is written as: y m,n =A m,n s m,n +w m,n Among them, y m,n A is the echo vector received by the nth receiving element from the mth transmitting element; m,n is the reference matrix derived from this perspective; σ m,n =[σ m,n,1 σ m,n,2 …σ m,n,L ] T is the scattering coefficient vector of the viewing angle; w m,n is the noise vector; According to the position of the transmitting and receiving array elements and the position of the imaging unit, N reference matrices A1, A2, ..., A are obtained. N , concatenate the N reference matrices in the time dimension to obtain an expanded reference matrix A = [A1, A2, ..., A N ] T .

3. The MIMO multi-view radar correlation imaging method based on image entropy according to claim 1 is characterized in that: The implementation process of step (2) is as follows: The scattering intensity vector σ of a resolution unit at different viewing angles l =[σ l,1 …σ l,M×N ] obeys the Gaussian mixture distribution model, and its probability density function expression is as follows: Where K represents the number of Gaussian components; μ i ,∑ i Represents the mean and covariance matrix of each Gaussian component, π i Represents the weight coefficient of each Gaussian component; For the nth receiving array element, the echo signal it receives comes from all M transmitting array elements: Among them, σ n is the comprehensive imaging result of the nth receiving array element; Δσ m,n =σ m,n -σ n It means that for the nth receiving channel, the imaging result corresponding to the mth transmitting channel is equal to σ n The difference between A and B is the fluctuation term of the viewing angle. n is the reference matrix corresponding to the nth receiving array element; The MIMO system is adopted to obtain the echo data of N receiving channels. The echo data of multiple receiving channels are spliced ​​in the time dimension by using the data layer expansion fusion method. The randomness of the radiation field reference signal is improved by expanding the spatial dimension. The expanded dimensional imaging equation based on the fluctuation of the resolution unit scattering intensity is derived as follows: remember Where y is the expanded-dimensional echo vector of MIMO radar correlation imaging; A n is the reference matrix derived from the nth receiving array element, diag(A) is the matrix of A1, A2, …, A N is a block diagonal matrix with diagonal elements; For is a block diagonal matrix with diagonal elements; Δσ n =σ n -σ is the imaging result σ corresponding to the nth receiving channel n The difference between the comprehensive inversion imaging result σ of the MIMO system.

4. The MIMO multi-view radar correlation imaging method based on image entropy according to claim 1, characterized in that: The implementation process of step (3) is as follows: In the correlation imaging model, find a solution that makes the number of non-zero elements in the scattering coefficient matrix as small as possible, then the objective function can be written as the minimization of the l0 norm of σ. Replace the objective function in the optimization problem with the l1 norm for solution, and the model is transformed into: In the formula, The l1-norm optimization method is a linear convex problem; The new definition of image entropy in the sparse reconstruction system is adopted, and the probability defined by the gray value frequency in the traditional image entropy is replaced by the probability defined by l p Entropy defined in norm form; for a radar imaging space consisting of L imaging grids, the scattering coefficient of the lth grid is σ l , the corresponding probability is get By writing the image entropy into the objective function, the imaging model of MIMO radar correlation imaging under the fluctuation of target resolution unit scattering intensity is obtained: Select λ1 and λ2, λ2 = μλ1, and express the above formula in a more general way to obtain the target expression:

5. The MIMO multi-view radar correlation imaging method based on image entropy according to claim 1, characterized in that: The implementation process of step (4) is as follows: set up In the (k+1)th iteration, based on the solution σ of the kth iteration k , using the quadratic approximation: Where t = (2λ max (A H A)) -1 ; After adding l1 norm and image entropy constraints, the optimization model is: make For h(|σ l |) Do a first-order Taylor expansion at: According to the principle of ISTA, the final iterative formula is obtained:

6. A device, characterized in that: comprising a memory and a processor, wherein: A memory for storing computer programs that can be run on the processor; A processor is used to execute the steps of the MIMO multi-view radar correlation imaging method based on image entropy as described in any one of claims 1 to 5 when running the computer program.

7. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by at least one processor, implements the steps of the MIMO multi-view radar correlation imaging method based on image entropy as described in any one of claims 1 to 5.

Images