Design method of multi-view MIMO radar correlation imaging radiation field based on image entropy

Abstract

The application discloses a kind of based on image entropy's multi-view MIMO radar correlation imaging radiation field design method, considering the fluctuation of the scattering intensity of each resolution unit of target in imaging scene under multi-view condition, the scattering intensity of each resolution unit of target is statistically modeled, from the overall process of signal transmission, receiving, processing, the condition number of Gram matrix of radiation field and the image entropy of imaging are constrained, through continuous iteration optimization, find a kind of to target grid energy dissipation has better inhibitory effect, imaging is relatively stable MIMO radar transmission waveform and the array pattern of transmission array, improve the correlation between radiation field reference matrix and signal echo.The application uses the array pattern of optimized transmission signal waveform and transmission array, and can be radar correlation imaging to the fluctuation of each resolution unit scattering intensity target, enhances the randomness of radiation field, reduces the energy dissipation degree of target grid, reduces imaging error, and can obtain better radar imaging result.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of radar correlation imaging signal processing, and considers the fluctuation of the scattering intensity of each resolution unit of a target, and particularly relates to a multi-view MIMO radar correlation imaging radiation field design method based on image entropy. BACKGROUND

[0002] Radar has the working ability of all-weather, all-day, and long distance, and is paid more attention and widely researched. In recent years, by drawing lessons from the principle of optical ghost imaging, radar correlation imaging has developed into a new technology of high-resolution staring imaging, which does not depend on the relative motion between radar and target, and provides a brand-new perspective and solution for the bottleneck problems of imaging of existing radar imaging technology for static, quasi-static targets and non-cooperative targets.

[0003] Radar correlation imaging divides the imaging plane into a grid, forms a two-dimensional random radiation field in space and time in the imaging area by transmitting a randomly modulated signal, and obtains the distribution of the target by correlating the echo with the reference signal of each grid. The two-dimensional random radiation field in space and time provides sufficient information for the resolution and imaging of the target, and determines the quality of the correlation imaging. Constructing the two-dimensional random radiation field in space and time is a prerequisite for radar correlation imaging. Many scholars have researched the problem of weak randomness of the radiation field in radar correlation imaging, and have achieved good results. The existing research analyzes the representation of the randomness of the radiation field, such as using the effective rank to evaluate the randomness of the radiation field, mainly designing the waveform of the transmitted signal and the array pattern of the radar antenna array; such as the waveform design based on the condition number, such as the waveform and transmitting array pattern design based on the minimization of the difference between the Gram matrix of the radiation field and the unit matrix, etc.

[0004] The performance, viewing angle, polarization mode, etc. of the target itself will cause the change of the overall RCS of the point target, and also cause the change of the scattering intensity of each resolution unit of the extended target, and the comprehensive value of each pixel of the radar image is obtained after radar imaging. When considering the fluctuation of the scattering intensity of each resolution unit of the extended target, it is found that the reconstructed image is more chaotic, indicating that the correlation between the reference matrix and the scattering echo is degraded, and the target grid energy is dissipated. The scattering characteristics of each resolution unit of the target are not considered in the previous research when designing the radiation field, so the imaging using the optimized random radiation field cannot have good reconstruction effect.

[0005] This invention addresses the poor performance of MIMO radar in correlated imaging of targets with fluctuating scattering intensity across different resolution cells, and the degradation of the correlation between the reference matrix and the echo. It proposes a multi-view MIMO radar correlated imaging radiation field design method based on image entropy. Starting from the overall process of signal transmission, reception, and processing, the scattering intensity of each resolution cell of the target is statistically modeled. A MIMO radar transmission waveform and transmission array configuration that effectively suppresses energy dissipation from the target grid and provides stable imaging is sought, thereby improving the correlation between the radiation field reference matrix and the signal echo, and ultimately enhancing image quality. Summary of the Invention

[0006] Purpose of the invention: This invention provides a multi-view MIMO radar correlation imaging radiation field design method based on image entropy, which solves the problem that traditional radar correlation imaging radiation field design does not consider the scattering characteristics of actual targets and has poor imaging effect when imaging targets with fluctuating scattering intensity of each resolution unit.

[0007] Summary of the Invention: The multi-view MIMO radar correlation imaging radiation field design method based on image entropy described in this invention includes the following steps:

[0008] (1) Set up the scene for MIMO radar correlation imaging, and generate the transmitted signal waveform and the position of the transmitted array elements.

[0009] (2) Based on the waveform of the transmitted signal, the positions of the transceiver array elements, and the positions of the imaging units, N reference matrices A are obtained. n By concatenating the N reference matrices along the time dimension, we obtain the extended-dimensional reference matrix A = [A1; A2; ...; A...]. n ]; Statistical modeling is performed on each resolution cell of the target. After the transmitted signal is scattered by each resolution cell, it is sampled at the receiving end for time J to obtain N echo vectors y = [y1; y2; ...; y n The extended-dimensional echo vector y is obtained by splicing the vectors together.

[0010] (3) Based on the multi-view MIMO radar correlation imaging algorithm of sparse reconstruction, the extended dimension reference matrix and the extended dimension echo are jointly processed to obtain the scattering intensity value of each resolution unit of the imaging scene.

[0011] (4) Set the maximum number of iterations Z max Using the condition number of the Gram matrix of the radiation field and the image entropy obtained from the scattering intensity of each resolution unit of the imaging scene as the objective function, the waveform of the transmitted signal and the array shape of the transmission array are iteratively optimized by constraining the condition number of the Gram matrix of the radiation field and the image entropy.

[0012] (5) If the iteration number is not reached, return to step (2), if the iteration number is reached, output the optimal signal waveform and the array pattern of the transmitting array.

[0013] Further, the scene setting implementation process of step (1) is as follows:

[0014] The number of MIMO radar transmitting array elements is M, the number of receiving array elements is N, the transmitting array is a random linear array, the receiving array is a uniform linear array, and the transmitting signal uses a random frequency hopping signal; the imaging plane is divided into L=x num *y num imaging units, x num represents the number of transverse imaging units, y num represents the number of longitudinal imaging units, and the number of time samples is J.

[0015] Further, the transmitting signal waveform of step (1) is:

[0016]

[0017] wherein the random frequency hopping signal waveform transmitted by the mth transmitting array element is S m (t), which is composed of Q sub-pulses, q represents the qth sub-pulse, f c represents the carrier frequency, Δt and Δf represent the pulse duration and the minimum frequency interval, G is a positive integer, c m,q is the frequency hopping code corresponding to each sub-pulse, c m,q ∈{0,1,…G-1}; u(t) represents a gate function that takes the value 1 within 0<t<Δt and takes the value 0 at other times.

[0018] Further, the transmitting array element position of step (1) is:

[0019] The position of the first array element in the transmitting array is p1=0, the position of the last array element is p M =F, the positions of other array elements except the first and last array elements are p m ∈{1,2,···F-1},m=2,3,···M-1; F is a positive integer;

[0020] The position P of the transmitting array element to be optimized is represented by the following formula:

[0021] P={p m |p m ∈{1,2,…F-1},if i≠j then p i ≠p j ,m,i,j=2,3,…M-1} (3)。

[0022] Further, the step (2) is implemented by the following formula:

[0023] The scattering intensity of each resolution unit is statistically modeled using a complex Gaussian model, σ=A0e jφ , where σ is subject to a complex Gaussian distribution, the amplitude A0 of σ is subject to a Rayleigh distribution, and the phase φ of σ is subject to a uniform distribution:

[0024]

[0025] where f(A0) is the probability density of the amplitude, f(φ) is the probability density of the phase, A n (r l ,t) is a reference signal, y n (t) is a return signal, S m (t) represents the transmission signal of the mth transmission array element, c is the propagation speed of electromagnetic waves, is the path delay of the mth transmission array element through the lth imaging unit to the nth receiving array element; is the scattering intensity of the lth imaging unit in the perspective of the mth transmission array element to the nth receiving array element; w n (t) is the noise of the nth receiving channel.

[0026] Further, the step (3) is implemented by the following formula:

[0027]

[0028] where ||·||1 represents the l1 norm of a vector, ||·||2 represents the l2 norm of a vector, μ is a regularization parameter used to control the balance between the l1 regularization term and the data fitting term; by minimizing the objective function, the imaging target scattering vector satisfying the sparse condition is obtained.

[0029] Further, the step (4) is implemented by the following formula:

[0030]

[0031] where A is the extended radiation field reference matrix, A H represents the conjugate transpose of A, cond(·) represents the condition number, h(v) represents the image entropy of imaging, v l represents the scattering intensity estimate of each imaging unit, ||·||1 represents the l1 norm of a vector, ξ represents the optimization weight, C is the frequency hopping coding matrix, and P is the non-repeating transmission array element position coding set.

[0032] Further, the iterative optimization solution of step (4) is implemented by a grey wolf optimization algorithm, and the process is as follows:

[0033] The GWO algorithm uses the following equation to simulate the surrounding behavior:

[0034] D = |C' · X p (z) - X(z) | (10)

[0035] X(z + 1) = X p (z) - E · D (11)

[0036] E = 2a · ε1-a (12)

[0037] C' = 2 · ε2 (13)

[0038] where z represents the current iteration number, X p (z) is the position vector of the prey, X(z) represents the position vector of the grey wolf, a is the convergence factor, and ε1, ε2 are random vectors between [0, 1];

[0039] The GWO algorithm uses the following equation to simulate the hunting behavior:

[0040] D α = |C'1 · X α - X | D β = |C'2 · X β - X | D δ = |C'3 · X δ - X | (14)

[0041] X1= X α - E1 · D α X2= X β - E2 · D β X3= X δ - E3 · D δ (15)

[0042]

[0043] Each set of target functions obtained from different transmit waveforms and transmit array element positions is called an individual, and the α, β, and δ wolves are the three individuals with the best, second-best, and third-best performance, respectively. D α , D β , and D δ are the differences between the remaining individuals and the top three individuals, X α , X β , and X δ are the transmit waveforms and transmit array element positions corresponding to the top three individuals, C'1, C'2, and C'3 are random vectors generated during the process, X is the transmit signal waveform and transmit array pattern of the individual, and X(z + 1) is the transmit signal waveform and transmit array pattern corresponding to the updated individual.

[0044] Advantages: Compared with the prior art, the advantages of the present application are:

[0045] The present application adopts a MIMO radar correlation imaging system, considers the scattering characteristics of actual targets, and aims at the problem that the effect is poor when imaging the target with fluctuation of scattering intensity of each resolution unit, the correlation of the reference matrix and the echo is degraded, and from the whole process of signal transmission, reception and processing, the transmission signal waveform and the array pattern of the radiation field are designed by constraining the condition number of the Gram matrix of the extended reference matrix of the radiation field and the image entropy of imaging. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 The flowchart of the present application;

[0047] Figure 2 The waveform of the random frequency hopping signal used in the present application;

[0048] Figure 3 The position of the transmitting array element in the present application;

[0049] Figure 4 The MIMO radar correlation imaging principle diagram of the present application;

[0050] Figure 5 The ideal imaging scene diagram;

[0051] Figure 6 The imaging result diagram obtained when imaging the same target scene using different transmission waveforms and the positions of the transmitting elements; (a) is the result diagram obtained by imaging using the unoptimized transmission signal random waveform and the transmitting element random position; (b) is the result diagram obtained by imaging using the transmission signal waveform and the transmitting array pattern optimized by taking the maximum effective rank as the objective function; (c) is the result diagram obtained by imaging using the transmission signal waveform and the transmitting array pattern optimized by taking the difference between the Gram matrix of the radiation field and the unit matrix as the objective function; (d) is the result diagram obtained by imaging using the transmission signal waveform and the transmitting element pattern optimized by taking the condition number of the Gram matrix of the radiation field as the objective function; (e) is the result diagram obtained by imaging using the transmission signal waveform optimized by taking the condition number of the Gram matrix of the radiation field and the image entropy of the imaging diagram as the objective function; (f) is the result diagram obtained by imaging using the transmission signal waveform and the transmitting array pattern optimized by taking the condition number of the Gram matrix of the radiation field and the image entropy of the imaging diagram as the objective function. DETAILED DESCRIPTION

[0052] The application will be described further to with reference to the accompanying drawings.

[0053] The application provides a multi-view MIMO radar correlation imaging radiation field design method based on image entropy, comprising the following steps:

[0054] Step 1: setting the scene of radar correlation imaging, setting the number and type of transmitting and receiving array elements, the division method of the imaging grid, the type of transmitted signals, the number of time samples, etc.

[0055] The number of MIMO radar transmitting array elements is M, the number of receiving array elements is N, the transmitting array is a random linear array, the receiving array is a uniform linear array, and a set of random frequency hopping signals is transmitted at the transmitting antenna end. The imaging plane is divided into L=x num *y num imaging units, assuming that the target scattering center is located at the center of the imaging unit, x num represents the number of transverse imaging units, x num represents the number of longitudinal imaging units, and the number of time samples is J.

[0056] The waveform of the random frequency hopping signal is specifically as shown in Figure 2 .

[0057]

[0058] Among them, the random frequency hopping signal waveform transmitted by the mth transmitting array element is S m (t), which is composed of Q sub-pulses, q represents the qth sub-pulse, f c represents the carrier frequency, Δt and Δf represent the pulse duration and the minimum frequency interval, G is a positive integer, c m,q is the frequency hopping code corresponding to each sub-pulse, c m,q ∈{0,1,…G-1}. u(t) represents a gate function that takes the value 1 within 0<t<Δt and takes the value 0 at other times.

[0059] The position of the transmitting array element to be optimized is expressed by the formula:

[0060] P={p m |p m ∈{1,2,…F-1},if i≠j then p i ≠p j ,m,i,j=2,3,…M-1} (3)

[0061] The array pattern of the transmitting array is as shown in Figure 3 , wherein the solid circle is the position of the transmitting array, the hollow circle is the gap position between the array elements. λ is the signal wavelength, and the array aperture is The position of the first array element p1=0, the position of the last array element p M =F, the positions of other array elements to be optimized except the first and last array elements p m ∈{1,2,···F-1},m=2,3,···M-1, and the positions of each array element are not repeated, F is a positive integer.

[0062] Step 2: The schematic diagram of radar correlation imaging is shown in Figure 4 The transmit signal waveform and the transmit array element position are generated, the time delay of each path corresponding to L imaging units is obtained according to the transmit signal waveform, the position of the transmit-receive array element and the position of the imaging unit, then the reference matrix corresponding to each receiving array element is obtained, and then N reference matrices [A n The N reference matrices are spliced in the time dimension to obtain the expanded reference matrix A=[A1;A2;···;A n ]. The target is statistically modeled for each resolution unit. After the scattering of the transmit signal by each resolution unit, J time samples are obtained at the receiving end, and N echo vectors y n are obtained, which are spliced to obtain the expanded echo vector y=[y1;y2;···;y n ]. The echo signal can be regarded as a linear combination of the reflection waveforms of all resolution units to all transmit array elements.

[0063] The scattering intensity σ of each resolution unit is statistically modeled using a complex Gaussian model, σ=A0e jφ , σ obeys a complex Gaussian distribution, the amplitude A0 of σ obeys a Rayleigh distribution, and the phase φ of σ obeys a uniform distribution. The probability density expression f(A0) of the amplitude is shown in equation (3), and the probability density f(φ) of the phase is shown in equation (4):

[0064]

[0065] wherein, A n (r l ,t) is a reference signal, r l is the position vector of the center of the lth imaging unit in the imaging scene, R m is the position vector of the mth transmit array element, R n is the position vector of the nth receiving array element, y n (t) is an echo signal, S m (t) represents the transmit signal of the mth transmit array element, c is the propagation speed of electromagnetic waves, is the path time delay of the mth transmit array element through the lth imaging unit to the nth receiving array element; is the scattering intensity value of the lth imaging unit in the transmit-receive angle of the mth transmit array element to the nth receiving array element; w n(t) is the noise of the nth receiving channel.

[0066] Step 3: based on the sparse reconstruction multi-view MIMO radar correlation imaging algorithm, the extended reference matrix and the extended echo are jointly processed to obtain the scattering intensity value of each resolution unit of the imaging scene.

[0067]

[0068] Wherein, ||·||1 represents the l1 norm of the vector, ||·||2 represents the l2 norm of the vector, and mu is a regularization parameter, used to control the balance between the l1 regularization term and the data fitting term. By minimizing the objective function, the imaging target scattering vector satisfying the sparse condition is obtained.

[0069] Step 4: set the maximum iteration number Z max With the condition number of the radiation field Gram matrix and the image entropy obtained from the scattering intensity value of each resolution unit of the imaging scene as the objective function, the waveform of the transmitting signal and the array pattern of the transmitting array are iteratively updated by constraining the condition number of the radiation field Gram matrix and the image entropy. The transmitting signal waveform and the transmitting array pattern are optimized, as shown in formula (9):

[0070]

[0071] Wherein, A is the extended reference matrix, A H Indicates the conjugate transpose of A, cond(·) indicates the condition number, h(v) indicates the image entropy of the imaging, v l Indicates the scattering intensity estimation value of each imaging unit, ||·||1 indicates the l1 norm of the vector, and xi indicates the optimization weight. C is a frequency hopping coding matrix, and P is a non-repeating transmitting array element position coding set. The waveform design is performed by optimizing the frequency hopping coding matrix of the random frequency hopping signal, and the array pattern design is performed by optimizing the position of the transmitting array element. The condition number of the radiation field Gram matrix and the image entropy of the imaging are constrained, the ill-conditioned degree of the reference matrix is reduced, the randomness of the radiation field and the imaging effect are improved, and the energy dissipation degree of the target grid is reduced.

[0072] The present application uses grey wolf optimization algorithm (GWO, Grey Wolf Optimizer) for iterative optimization solution. The GWO algorithm uses the following equation to simulate the surrounding behavior:

[0073] D=|C'·X p (z)-X(z)| (10)

[0074] X(z+1)=X p (z)-E·D (11)

[0075] E=2a·ε1-a (12)

[0076] C' = 2 · ε2 (13)

[0077] where z denotes the current iteration number, X p (z) is the position vector of the prey, X(z) denotes the position vector of the grey wolf, a is the convergence factor, and ε1, ε2 are random vectors between [0, 1].

[0078] The GWO algorithm simulates hunting behavior using the following equations:

[0079] D α = |C'1 · X α -X|D β = |C'2 · X β -X|D δ = |C'3 · X δ -X| (14)

[0080] X1= X α -E1·D α X2= X β -E2·D β X3= X δ -E3·D δ (15)

[0081]

[0082] where the optimal solution, the second optimal solution, and the third optimal solution are called alpha, beta, and delta wolves, respectively, and D α , D β , D δ denote the distances between alpha, beta, and delta wolves and other individuals, X α , X β , X δ are the current position vectors of alpha, beta, and delta wolves, C'1, C'2, and C'3 are random vectors obtained from equation (12), X is the current position of the grey wolf, and X(z+1) is the updated position of the grey wolf.

[0083] Each set of objective functions obtained by different transmit waveforms and transmit element positions in this problem is called an individual, and alpha, beta, and delta wolves are the three individuals with the best, second best, and third best performance, respectively, D α , D β , D δ are the differences between the remaining individuals and the top three individuals, X α , X β , X δThe three individuals corresponding to the emission waveform and the element position of the emission array are the top three, C'1, C'2 and C'3 are random vectors generated in the process, X is the emission signal waveform and the emission array pattern of the individual, and X(z+1) is the emission signal waveform and the emission array pattern corresponding to the individual after updating.

[0084] Step 5: If the number of iterations is not reached, return to step 2, and if the number of iterations is reached, output the optimal signal waveform and the pattern of the emission array.

[0085] The whole iteration optimization process is: 1) initialization to generate random individuals, including emission signal waveform coding and element position coding; 2) according to the emission signal waveform and the element position, the radiation field reference matrix and the echo are calculated, and the scattering intensity values of each resolution unit are obtained through sparse reconstruction, the fitness of all individuals, that is, the target function, is calculated, and the positions of alpha, beta and delta are recorded; 3) a, E and C' are generated, the positions of X1, X2, X3 and X(z+1) are updated according to formula (14) (15), and it is judged whether the element position is repeated or not, if the element position is repeated, the individual position is updated again; 4) according to the updated individual, the reference matrix, the echo and the scattering intensity value of each resolution unit are obtained, the fitness of all individuals after updating is calculated, and the positions of alpha, beta and delta are updated; 5) when the number of iterations is less than the maximum number of iterations, repeat 3) and 4). If the number of iterations reaches the maximum number of iterations, the optimal individual is output, including the optimal waveform coding and the emission element position coding.

[0086] Figure 5 When the simulation experiment is carried out, the ideal imaging scene graph when the scattering intensity of the target resolution unit satisfies the complex Gaussian distribution with the receiving and transmitting viewing angle is set, the mean value is 10 dBsm, and the variance is 8 dBsm; Figure 6The imaging result diagrams obtained by using different transmitting waveforms and positions of transmitting elements to image the same target scene, wherein (a) is the result diagram obtained by using random transmitting signal waveforms and random positions of transmitting elements to image; (b) is the result diagram obtained by using the transmitting signal waveforms and the transmitting array pattern optimized by taking the effective rank maximization as the objective function to image; (c) is the result diagram obtained by using the transmitting signal waveforms and the transmitting array pattern optimized by taking the difference between the Gram matrix of the radiation field and the unit matrix minimization as the objective function to image; (d) is the result diagram obtained by using the transmitting signal waveforms and the transmitting element pattern optimized by taking the condition number of the Gram matrix of the radiation field minimization as the objective function to image; (e) is the result diagram obtained by using the transmitting signal waveforms optimized by taking the condition number of the Gram matrix of the radiation field and the image entropy minimization as the objective function to image; (f) is the result diagram obtained by using the transmitting signal waveforms and the transmitting array pattern optimized by taking the condition number of the Gram matrix of the radiation field and the image entropy minimization as the objective function to image. It can be seen by comparison that the imaging effect is the best, the target grid energy dissipation is the lowest, and the imaging quality is the best when the transmitting waveforms and the positions of transmitting elements obtained by the application are used to image.

[0087] The above only describes the preferred embodiments of the present application, and it should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for designing the radiation field of multi-view MIMO radar correlation imaging based on image entropy, characterized in that, Comprise the following steps: (1) carry out the scene setting of MIMO radar correlation imaging, produce transmitting signal waveform and transmitting array element position, (2) According to the waveform of the transmitted signal, the position of the transceiving element, and the position of the imaging unit, N reference matrices A are obtained n The N reference matrices are spliced in the time dimension to obtain an extended reference matrix A = [A1; A2; ···; A n ];Statistical modeling of each resolution unit of the target, after the scattering of the transmitted signal through each resolution unit, the received signal is sampled J times at the receiving end to obtain N echo vectors y = [y1; y2; ···; y n ],and an extended echo vector y is obtained after splicing. (3) the multi-view MIMO radar correlation imaging algorithm based on sparse reconstruction is used to jointly process the extended dimension reference matrix and the extended dimension echo, so that the scattering intensity value of each resolution unit of the imaging scene is obtained; (4) Set the maximum number of iterations Z max With the condition number of the Gram matrix of the radiation field and the image entropy obtained from the scattering intensity values of each resolution unit of the imaging scene as the objective function, the waveform of the transmitted signal and the array pattern of the transmitting array are iteratively optimized and solved by constraining the condition number of the Gram matrix of the radiation field and the image entropy. (5) if the number of iterations is not reached, return to step (2), if the number of iterations is reached, output the optimal signal waveform and the array of transmitting array; The step (4) is realized by the following formula: where A is the extended radiation field reference matrix, A H denotes the conjugate transpose of A, cond(·) denotes the condition number, h(v) denotes the imaging image entropy, v l denotes the scattering intensity estimate of each imaging unit, ||·||1 denotes the l1 norm of the vector, ξ denotes the optimization weight, C is the frequency hopping coding matrix, and P is the non-repeating transmitting element position coding set.

2. The image entropy based multi-view MIMO radar correlated imaging radiated field design method of claim 1, wherein, The scene setting of step (1) is realized as follows: The MIMO radar has M transmitting array elements and N receiving array elements. The transmitting array is a random linear array, the receiving array is a uniform linear array, and a random frequency hopping signal is used as the transmitting signal. The imaging plane is divided into L=x num *y num imaging units, x num denoting the number of transverse imaging units, y num denoting the number of longitudinal imaging units, and J denoting the number of time samples.

3. The image entropy based multi-view MIMO radar correlated imaging radiated field design method of claim 1, wherein, The transmitting signal waveform of step (1) is as follows: wherein the random frequency hopping signal waveform transmitted by the mth transmitting element is S m (t), consisting of Q sub-pulses, q denotes the qth sub-pulse, f c represents the carrier frequency, Δt and Δf represent the pulse duration and the minimum frequency interval, G is a positive integer, c m,q is the frequency hopping code corresponding to each sub-pulse, c m,q ∈{0,1,…G-1};u(t) represents a gate function that takes the value 1 within 0<t<Δt and takes the value 0 at other times.

4. The image entropy based multi-view MIMO radar correlated imaging radiated field design method of claim 1, wherein, The transmitting array element position of step (1) is as follows: The position of the first element in the transmitting array is p1=0, and the position of the last element is p M = F, the position of other elements except the first and last elements is p m ∈ {1, 2, ··· F-1}, m = 2, 3, ··· M-1; F is a positive integer; The position P of the transmitting array element to be optimized is represented by the following formula: P = {p m |p m ∈{1,2,…F-1},if i≠j then p i ≠p j ,m,i,j = 2,3,…M-1} (3).

5. The method of claim 1, wherein the method is based on image entropy of the multi-view MIMO radar correlation imaging radiated field design. The realization process of the statistical modeling of each resolution unit of the target in the step (2) is as follows: The scattering intensity σ of each resolution cell is statistically modeled using a complex Gaussian model, σ = A0e jφ , where σ follows a complex Gaussian distribution, the amplitude A0of σ follows a Rayleigh distribution, and the phase φ of σ follows a uniform distribution: where f(A0) is the probability density of the amplitude, f(φ) is the probability density of the phase, A n (r l , t) is the reference signal, y n (t) is the echo signal, S m (t) represents the transmitting signal of the mth transmitting array element, c is the propagation speed of electromagnetic wave, is the path delay of the mth transmitting array element through the lth imaging unit to the nth receiving array element; is the scattering intensity of the lth imaging unit in the perspective of the mth transmitting array element to the nth receiving array element; w n (t) is the noise of the nth receiving channel.

6. The method of claim 1, wherein, The step (3) is realized by the following formula: Wherein, ||·||1 represents the l1 norm of vector, ||·||2 represents the l2 norm of vector, μ is a regularization parameter, used to control the balance between l1 regularization term and data fitting term;By minimizing the objective function, the imaging target scattering vector satisfying the sparse condition is obtained.

7. The method of claim 1, wherein, The iterative optimization solution of step (4) is realized by grey wolf optimization algorithm, and the process is as follows: GWO algorithm uses the following equation to simulate the surrounding behavior: D = |C' - X p (z) - X(z) | (10) X(z + 1) = X p (z) - E - D (11) E=2a·ε1-a (12) C'=2·ε2 (13) where z denotes the current iteration number, X p (z) is the position vector of the prey, X(z) denotes the position vector of the grey wolf, a is the convergence factor, and ε1, ε2 are random vectors between [0, 1]; GWO algorithm uses the following equation to simulate hunting behavior: D α = |C'1 · X α - X | D β = |C'2 · X β - X | D δ = |C'3 · X δ - X | (14) X1= X α - E1 D α X2= X β - E2 D β X3= X δ - E3 D δ (15) Each group of objective function obtained by different transmit waveform and transmit array element position is called individual, then α, β, δ wolf are three individuals with the best, the second best and the third best performance, D α , D β , D δ is the gap between the remaining individuals and the top three individuals, X α , X β , X δ is the transmit waveform and the element position of the transmit array corresponding to the three individuals with the top three performance, C'1, C'2, C'3 are random vectors generated in the process, X is the transmit signal waveform and the transmit array pattern of the individual, X(z+1) is the transmit signal waveform and the transmit array pattern corresponding to the individual after updating.

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