Distributed sparse MIMO array structure design method suitable for near-field imaging

Abstract

The invention relates to a distributed sparse MIMO array structure design method suitable for near-field imaging, and the method comprises the steps: obtaining an initial value of each particle and an initial structure of a sparse MIMO array based on the distribution characteristics of the sparse MIMO array and at least one to-be-optimized variable with the size determined, combining a Voronoi diagram method with effective weight distribution of a space-variant frequency spectrum to propose a novel optimization function, calculating the fitness of each particle, and determining the position and the global optimal fitness; calculating an initial temperature and a global optimal position of an annealing algorithm; and updating the speed and the fitness, carrying out annealing operation until an iteration termination condition is met, and generating an optimized single-module distributed sparse MIMO array. Therefore, the problems that in related technologies, an MIMO array design method has obvious grating lobes in near-field SAR imaging, and SAR image performance is reduced due to limitation of optimization of an objective function are solved.

Description

Technical Field

[0001] This application relates to the field of millimeter-wave near-field imaging technology, and particularly to a design method for a distributed sparse MIMO array structure suitable for near-field imaging. Background Art

[0002] Millimeter-wave signals have the ability to penetrate obstacles for high-resolution imaging, and due to their weak electromagnetic radiation characteristics, they do not cause harm to the human body. SAR (Synthetic Aperture Radar) imaging technology can effectively combine a small number of actual array elements into a larger synthetic aperture through mechanical movement, thereby improving the imaging performance of the system. Therefore, millimeter-wave SAR imaging technology has been widely applied and studied in multiple fields, including through-wall imaging, ground-penetrating radar, non-destructive testing, medical imaging, and security inspection, etc.

[0003] Among different array operating modes, compared with traditional SISO (Single-Input Single-Output) and SIMO (Single-Input Multiple-Output) arrays, MIMO (Multiple-Input Multiple-Output) arrays have significant performance advantages. MIMO arrays can achieve better imaging performance, including a wider illumination area and a wider dynamic range, etc., by using fewer transmit and receive array elements. In addition, in practical applications, modular design can greatly reduce the complexity of system design and manufacturing, and improve the flexibility of system expansion. For the MIMO array topology, modularity means a distributed MIMO array. Therefore, in millimeter-wave SAR imaging systems, distributed MIMO arrays have become the mainstream solution.

[0004] The sparsification of MIMO arrays is also an important research topic. A uniformly periodic MIMO array is prone to causing obvious grating lobes in the imaging results, while a sparse MIMO array can effectively suppress the generation of grating lobes through a non-uniform topological structure. In addition, the sparsification of MIMO arrays means using fewer array elements and a larger element spacing. Reducing the number of array elements can not only reduce the manufacturing cost of MIMO arrays, but also reduce the amount of data of the received signals, thus alleviating the burden on the data transmission system and the imaging processing process and providing the possibility for real-time imaging. And a larger element spacing can improve the isolation between different transmit and receive array elements and reduce the interference of antenna coupling on SAR images. In traditional MIMO array designs, it is usually required that the spacing between transmit and receive array elements is less than the wavelength to suppress grating lobes in SAR images. However, for high-frequency imaging systems, too small an element spacing poses a huge challenge to the design of transmit and receive antennas. On the contrary, increasing the element spacing can not only relieve the burden of antenna design, but also provide more design space for achieving an ideal antenna pattern.

[0005] Traditional design methods for the topological structure of sparse MIMO arrays usually sample the far-field approximation to simplify the difficulty of the problem. Under far-field conditions, the imaging performance of a MIMO array is equivalent to that of a virtual array formed by the convolution of transmit and receive arrays. Therefore, the most common design method is to design the virtual array as a uniformly dense distribution and inversely design the uniformly periodic transmit and receive arrays. However, in near-field imaging applications, SAR images have severe spatially variant characteristics, and there is a large deviation between the imaging performance of the virtual array and the MIMO array. The array design results of the above method will generate obvious grating lobes in SAR images.

[0006] Most of the remaining sparse MIMO array topology design methods adopt an optimization idea, and the main differences lie in the optimization objective function and the optimization method. Moreover, according to different requirements, the optimization constraints will also change accordingly. The key to the design of the objective function lies in whether it can accurately describe the global imaging performance of the MIMO array for different targets. The objective functions adopted by traditional methods include comprehensive indicators such as the degree of coincidence between the PSF (Point Spread Function) of the millimeter-wave imaging system and the ideal PSF, the main lobe width of the PSF, and the PSR (Peak Side Lobe Ratio), as well as the entropy or sharpness of the SAR image, etc. However, these objective functions all have obvious limitations. The first two are both centered around the PSF, but the actual imaging scenario is complex, and the radiation pattern of the array element antenna and the spatial attenuation of the millimeter-wave signal will both have a great impact on the SAR image, and these factors are usually not considered in the above studies. In addition, using the PSF to evaluate the performance of the SAR image has obvious locality and is difficult to evaluate the overall performance indicators of extended targets. The latter directly evaluates the imaging performance based on the SAR image, but the large computational simulation task of extended targets makes the existing research still use ideal point targets for PSF simulation, without avoiding the above disadvantages. Since the optimization objective function is usually multi-dimensional and non-linear, the focus of the optimization method lies in the local fast convergence ability and the ability to escape from local traps, so as to converge to the global optimal solution as quickly as possible. Existing optimization techniques usually adopt the PSO (Particle Swarm Optimization) algorithm, which can quickly achieve local convergence of the optimization process. In order to further prevent the optimization process from falling into local traps, the SA-PSO (Simulated annealing-Particle Swarm Optimization) algorithm is introduced. During the optimization process of the SA-PSO algorithm, non-current optimal solutions also have a probability of affecting the optimization iteration direction, thus enhancing the ability to escape from local traps. Summary of the Invention

[0007] The present application provides a design method for a distributed sparse MIMO array structure suitable for near-field imaging, so as to solve the problems in the related art, such as obvious grating lobes in the traditional MIMO array design method in near-field SAR imaging, and the degradation of SAR image performance caused by the limitations of the optimization objective function. By comprehensively analyzing the space-variant characteristics of the SAR image spectrum, innovatively combining the Voronoi diagram method with the effective weight distribution of the space-variant spectrum, and proposing a new topology optimization objective, that is, taking the degree of coincidence between the space-variant spectrum weight distribution of the SAR image and the ideal weight distribution as the optimization objective, and combining the simulated annealing-particle swarm optimization (SA-PSO) algorithm, the present invention realizes the optimized design of the distributed sparse MIMO array topology structure, overcomes the deficiencies of the traditional method in dealing with space-variant characteristics, and has significant innovation and practicality.

[0008] The first aspect of the present application provides an optimization method for the design of a distributed sparse MIMO array structure suitable for near-field imaging, including the following steps: determining at least one variable to be optimized based on the distribution characteristics and size of the sparse multiple-input multiple-output (MIMO) array, to initialize the positions and velocities, attention weights, learning factors, and annealing velocities of the particle swarm corresponding to the at least one variable to be optimized, and obtaining the initial values of each particle; obtaining the preliminary structure of the sparse MIMO array according to the initial values of each particle, and based on the preliminary structure, combining the Voronoi diagram method with the effective weight distribution optimization objective function of the space-variant spectrum, calculating the fitness of each particle according to the objective function, and determining the position and global optimal fitness of each particle; calculating the initial temperature of the annealing algorithm and determining the global optimal position based on the position and global optimal fitness of each particle; updating the velocity of each particle and calculating the new fitness based on the initial temperature of the annealing algorithm and determining the global optimal position, so as to perform the annealing operation until the iteration termination condition is satisfied, and generating an optimized single-module sparse MIMO array.

[0009] Optionally, in an embodiment of the present application, the calculating the fitness of each particle and determining the position and global optimal position of each particle based on the preliminary structure includes: quantifying the discrete spectrum distribution and its weight at different positions in the imaging space to calculate the local spectrum weight distribution of the SISO array, and respectively converting the two-dimensional spectrum weights of the sparse MIMO array and the SISO array into one-dimensional spectra with respect to the angular variable, so as to obtain the optimization objective function; calculating the fitness of each particle based on the objective function, and recording the position, global optimal position, fitness, and global optimal fitness of each particle.

[0010] Optionally, in an embodiment of the present application, the calculation formula for the discrete spectrum distribution and its weight is:

[0011]

[0012] where k x and k z are the spatial spectral wavenumber variables of the SAR image along the x and z axis directions respectively, R t and R r are the distances between the transmitting and receiving array elements and the spatial target point respectively, θ t and θ r are the azimuth angles of the spatial target point relative to the transmitting and receiving array elements respectively, θ t0 and θ r0 , R t0 and R r0 are the θ t , θ r , R t , R r variables corresponding to the point scatterer (x0, z0) respectively, f0 is the reflectivity, α(·) is the radiation pattern of the transmitting and receiving antennas, and β(k x , k z ) is the area of the polygon region segmented by the Voronoi diagram method.

[0013] Optionally, in an embodiment of the present application, the calculation formula for the fitness of each particle is:

[0014]

[0015] where x and z are spatial variables, k x and k z are the spatial spectral wavenumber variables of the SAR image along the x and z axis directions respectively, is the weight distribution of the local spectrum of the SAR image, is the local spectrum weight distribution of the SISO array, and ω(x, z), ω(θ) are the attention weight of the imaging quality at different spatial positions.

[0016] Optionally, in an embodiment of the present application, the iteration termination condition is that the number of loops reaches a preset maximum number of iterations or the objective function value is lower than a preset threshold.

[0017] The second aspect of the present application provides an optimization device for the design of a distributed sparse MIMO array structure suitable for near-field imaging, including: an initialization module, configured to determine at least one variable to be optimized based on the distribution characteristics and size of the sparse MIMO array, so as to initialize the positions and velocities of the particle swarms corresponding to the at least one variable to be optimized, the attention weight, the learning factor, and the annealing velocity, and obtain the initial values of each particle; a determination module, configured to obtain the preliminary structure of the sparse MIMO array according to the initial values of each particle, and based on the preliminary structure, combine the Voronoi diagram method with the effective weight distribution optimization objective function of the spatio-frequency spectrum, calculate the fitness of each particle according to the objective function, and determine the position and global optimal fitness of each particle; a calculation module, configured to calculate the initial temperature of the annealing algorithm and determine the global optimal position based on the position and global optimal fitness of each particle; a generation module, configured to update the velocity of each particle and calculate the new fitness based on the initial temperature of the annealing algorithm and the determined global optimal position, so as to perform the annealing operation until the iteration termination condition is satisfied, and generate an optimized single-module sparse MIMO array.

[0018] Optionally, in an embodiment of the present application, the module includes: a conversion unit, configured to quantify the discrete spectrum distribution and its weight at different positions in the imaging space, so as to calculate the local spectrum weight distribution of the SISO array, and convert the two-dimensional spectrum weights of the sparse MIMO array and the SISO array into one-dimensional spectra with respect to the angular variable respectively, so as to obtain the optimization objective function; a recording unit, configured to calculate the fitness of each particle based on the objective function, and record the position, global optimal position, fitness, and global optimal fitness of each particle.

[0019] Optionally, in an embodiment of the present application, the calculation formula for the discrete spectrum distribution and its weight is:

[0020]

[0021] where k x and k z are the spatial spectrum wave number variables of the SAR image along the x-axis and z-axis directions respectively, R t and R r are the distances between the transmitting and receiving array elements and the spatial target point respectively, θ t and θ r are the azimuth angles of the spatial target point relative to the transmitting and receiving array elements respectively, θ t0 and θ r0 , R t0 and R r0 are the θ t , θ r corresponding to the point scatterer (x0, z0) respectively, Rt , R r is a variable, f0 is the reflectivity, α(·) is the radiation pattern of the transmitting and receiving antennas, and β(k x , k z ) is the area of the polygon region divided by the Voronoi diagram method.

[0022] Optionally, in an embodiment of the present application, the calculation formula for the fitness of each particle is:

[0023]

[0024] where x and z are spatial variables, and k x , k z are the spatial spectral wave number variables of the SAR image along the x and z axis directions respectively, is the weight distribution of the local spectrum of the SAR image, is the local spectrum weight distribution of the SISO array, and ω(x, z), ω(θ) are the attention weight of the imaging quality at different spatial positions.

[0025] Optionally, in an embodiment of the present application, the iteration termination condition is that the number of loops reaches a preset maximum number of iterations or the objective function value is lower than a preset threshold.

[0026] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the method for designing a distributed sparse MIMO array structure applicable to near-field imaging as described in the above embodiments.

[0027] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the method for designing a distributed sparse MIMO array structure applicable to near-field imaging as described above.

[0028] An embodiment of the fifth aspect of the present application provides a computer program product, and the computer program product stores a computer program, and when the program is executed by a processor, it implements the method for designing a distributed sparse MIMO array structure applicable to near-field imaging as described above.

[0029] Embodiments of the present application can obtain the initial value of each particle based on the distribution characteristics and size of the sparse MIMO array, obtain the preliminary structure of the sparse MIMO array according to the initial value of each particle, and based on the preliminary structure, use the degree of coincidence between the spatial frequency spectrum weight distribution of the SAR image and the ideal weight distribution as the optimization objective to determine the position of each particle and the global optimal fitness, and combine the SA-PSO algorithm to determine the global optimal position, thereby realizing the optimized design of the distributed sparse MIMO array topology structure and overcoming the deficiencies of traditional methods in dealing with spatial variant characteristics. Thus, it solves the problems in related technologies that the far-field approximation method has a large deviation in near-field imaging, the near-field SAR image has severe spatial variant characteristics, resulting in a mismatch between the imaging performance of the virtual array and the MIMO array, leading to obvious grating lobes, and the PSO algorithm is prone to falling into a local optimal solution when facing a non-linear multi-dimensional objective function, resulting in a decline in the performance of the SAR image, etc.

[0030] Additional aspects and advantages of the present application will be given in part in the following description, will become apparent in part from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0032] Figure 1 is a flowchart of an optimization method for the distributed sparse MIMO array structure design according to an embodiment of the present application;

[0033] Figure 2 is a schematic diagram of the discretized spectrum and the Voronoi diagram method division according to an embodiment of the present application;

[0034] Figure 3 is a schematic diagram of the optimized sparse MIMO array structure composed of 12 transmitting array elements and 16 receiving array elements according to an embodiment of the present application;

[0035] Figure 4 is a schematic diagram of the distributed sparse MIMO array structure composed of 8 single-module sparse MIMO arrays according to an embodiment of the present application;

[0036] Figure 5 is a schematic diagram of the structure of an optimization device for the distributed sparse MIMO array structure design applicable to near-field imaging according to an embodiment of the present application;

[0037] Figure 6 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0039] A distributed sparse MIMO array structure design method applicable to near-field imaging in embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problems in the related art mentioned in the above background art, namely, the far-field approximation method has a large deviation in near-field imaging, the near-field SAR image has severe spatial-variant characteristics, which makes the imaging performance of the virtual array and the MIMO array mismatched, resulting in obvious grating lobes, and the PSO algorithm is prone to fall into a local optimal solution when facing a non-linear multi-dimensional objective function, resulting in a decline in the performance of the SAR image, etc., the present application provides a distributed sparse MIMO array structure design method applicable to near-field imaging. In this method, the initial value of each particle can be obtained based on the distribution characteristics and size of the sparse MIMO array. According to the initial value of each particle, a preliminary structure of the sparse MIMO array is obtained, and based on the preliminary structure, the degree of coincidence between the spatial-frequency spectrum weight distribution of the SAR image and the ideal weight distribution is used as the optimization objective to determine the position of each particle and the global optimal fitness, and the SA-PSO algorithm is combined to determine the global optimal position, thereby realizing the optimal design of the distributed sparse MIMO array topology structure and overcoming the deficiencies of traditional methods in dealing with spatial-variant characteristics. Thus, the problems in the related art, namely, the far-field approximation method has a large deviation in near-field imaging, the near-field SAR image has severe spatial-variant characteristics, which makes the imaging performance of the virtual array and the MIMO array mismatched, resulting in obvious grating lobes, and the PSO algorithm is prone to fall into a local optimal solution when facing a non-linear multi-dimensional objective function, resulting in a decline in the performance of the SAR image, etc., are solved.

[0040] Specifically, Figure 1 FIG. is a schematic flow chart of an optimization method for a distributed sparse MIMO array structure applicable to near-field imaging provided by an embodiment of the present application.

[0041] As Figure 1 shown, the optimization method for a distributed sparse MIMO array structure applicable to near-field imaging includes the following steps:

[0042] In step S101, at least one variable to be optimized determined based on the distribution characteristics and size of the sparse MIMO array is used to initialize the position and velocity, attention weight, learning factor, and annealing velocity of a particle swarm corresponding to at least one variable to be optimized, so as to obtain the initial value of each particle.

[0043] Specifically, the embodiments of the present application can determine the variables to be optimized based on the distribution characteristics and size of the sparse MIMO array. For example, given the total size of the array composed of eight modules, the spacing of a single module is fixed. Considering the symmetric distribution of 12 transmitting antennas and 16 receiving antennas within a single module, and for the convenience of the layout and wiring of radar chips, one radar chip controls three transmitting antennas and four receiving antennas around it. Therefore, the three transmitting antennas and four receiving antennas connected to the same radar chip are evenly arranged.

[0044] Furthermore, the variables to be optimized are obtained, including the spacing between receiving and transmitting array elements and the spacing within a single module of receiving and transmitting array elements, a total of four variables to be optimized. During the optimization process, first, the positions and velocities, attention weights, learning factors, and annealing velocities of the particle swarm (the four variables to be optimized) are initialized to obtain the initial values of each particle.

[0045] The embodiments of the present application can determine the variables to be optimized based on the distribution characteristics and size of the sparse MIMO array, ensuring that the initial conditions meet the actual requirements. Initializing the positions and velocities of the particle swarm, attention weights, learning factors, and annealing velocities ensures that the algorithm starts searching from multiple starting points, avoiding premature convergence to local optimal solutions.

[0046] In step S102, based on the initial values of each particle, a preliminary structure of the sparse MIMO array is obtained. Based on the preliminary structure, the objective function is optimized by combining the Voronoi diagram method and the effective weight distribution of the spatial frequency spectrum. The fitness of each particle is calculated according to the objective function, and the position and global optimal fitness of each particle are determined.

[0047] It can be understood that in the embodiments of the present application, the fitness can be the value of the optimization objective function, used to evaluate the performance of the array corresponding to the current particle position. The global optimal fitness can be the optimal value of the fitness among all particles, representing the best solution found during the current search process.

[0048] The embodiments of the present application can quantify the spectrum distribution through the Voronoi diagram method, accurately evaluate the local spectrum weight distribution of SAR images, and improve the imaging quality. By comparing the spectrum weights of the sparse MIMO array and the SISO array, the array topology structure can be effectively optimized, grating lobes can be suppressed, and the imaging performance can be improved.

[0049] Optionally, in an embodiment of the present application, based on the preliminary structure, the Voronoi diagram method is combined with the effective weight distribution of the empty frequency spectrum to optimize the objective function. The fitness of each particle is calculated according to the objective function, and the position and global optimal position of each particle are determined, including: quantifying the discrete spectrum distribution and its weight at different positions in the imaging space to calculate the local spectrum weight distribution of the SISO array, and converting the two-dimensional spectrum weights of the sparse MIMO array and the SISO array into one-dimensional spectra with respect to the angle variable respectively to obtain the optimized objective function; based on the objective function, calculating the fitness of each particle, and recording the position, global optimal position, fitness, and global optimal fitness of each particle.

[0050] It can be understood that the discrete spectrum distribution and its weight in the embodiment of the present application can refer to the signal frequency components and their importance or influence degree at different positions in the imaging space.

[0051] In the actual execution process, the embodiment of the present application can obtain the preliminary structure of the sparse MIMO array according to the initial values of the four particles that have been obtained, and then use the Voronoi diagram method to quantify the discrete spectrum distribution and its weight at different positions in the imaging space according to the formula, calculate the local spectrum weight distribution of the SISO array using the same method, and convert the two-dimensional spectrum weights of the sparse MIMO array and the SISO array into one-dimensional spectra with respect to the angle variable through integration respectively. The difference between the two is the loss function (optimized objective function). Subsequently, the fitness of each particle is calculated according to the optimized objective function, and the position, global optimal position, fitness, and global optimal fitness of each particle are recorded.

[0052] The embodiment of the present application can accurately evaluate the imaging performance of the array by quantifying the spectrum distribution and its weight, and using the spectrum weight difference between the sparse MIMO array and the SISO array as the objective function. It not only clarifies the optimization objective but also ensures that the algorithm can obtain the global optimal solution by recording the global optimal fitness.

[0053] Optionally, in an embodiment of the present application, the calculation formula for the discrete spectrum distribution and its weight is:

[0054]

[0055] where k x and k z are the spatial spectral wave number variables of the SAR image along the x and z axis directions respectively, that is, the corresponding terms in the spatial wave number domain of x and z. R t and R r are the distances between the transmitting and receiving array elements and the spatial target point respectively. θ t and θ r are the azimuth angles of the spatial target point relative to the transmitting and receiving array elements respectively. θ t0, θ r0 , R t0 , R r0 are respectively the θ t , θ r , R t , R r variables corresponding to the point scatterer (x0, z0), f0 is the reflectivity, α(·) is the radiation pattern of the transmitting and receiving antennas, and β(k x , k z ) is the area of the polygon region divided by the Voronoi diagram method.

[0056] It can be understood that in the embodiments of the present application, the Voronoi diagram method can be used to calculate the discrete spectrum distribution and its weight to quantify the effective influence range of the spectrum scatter points.

[0057] In the actual execution process, the embodiments of the present application can first analyze the spectrum characteristics of the near-field SAR imaging results of the distributed sparse MIMO array, providing theoretical support for evaluating the imaging performance of the MIMO array from the spectrum perspective.

[0058] Specifically, the coordinates of the transmitting and receiving array elements in the MIMO array are respectively expressed as p t =(x t , 0), p r =(x r , 0), and the point coordinates in the imaging area are expressed as p=(x, z). Considering the influence of the radiation pattern of the transmitting and receiving array elements and the signal transmission attenuation in practical applications, and the propagation process of the millimeter-wave signal satisfies the first-order Born approximation, the echo signal in the imaging system can be expressed as:

[0059]

[0060] In the formula, s(·) represents the echo signal. k is the wave number of the millimeter-wave signal. R t , R r respectively represent the distances between the transmitting and receiving array elements and the spatial target point. θ t , θ r respectively represent the azimuth angles of the spatial target point relative to the transmitting and receiving array elements. α(·) represents the radiation pattern of the transmitting and receiving antennas. f(·) represents the reflectivity distribution function of the spatial target, that is, the target function for SAR image restoration.

[0061] Furthermore, in order to analyze the local characteristics of the SAR image and its spatial spectrum, the spatial target is restricted to a single point scatterer, whose position is (x0, z0) and the reflectivity is f0, then the echo signal s0 generated by it is:

[0062]

[0063] In the formula, θt0 , θ r0 , R t0 , R r0 are respectively the θ t , θ r , R t , R r variables corresponding to the point scatterer (x0, z0).

[0064] Using BPA (Back Projection Algorithm) to reconstruct the above formula, the result is:

[0065]

[0066] wherein, is the estimate of f0.

[0067] Subsequently, perform a Fourier transform in the neighboring region of (x0, z0) to transform the SAR image from the spatial domain to the spatial frequency domain and obtain the local spectrum of the SAR image, that is:

[0068]

[0069] wherein, k x , k z respectively represent the spatial spectrum wave number variables of the SAR image along the x and z axis directions, that is, the corresponding terms in the spatial wave number domain of x and z. U(x0) and U(z0) represent the neighborhoods of x0 and z0 in the integral.

[0070] Furthermore, use POSP (Principle of Stationary Phase) to solve the above integral with respect to x and z, and the result is:

[0071]

[0072] wherein, (k x0 , k z0 ) is the stationary point in POSP and satisfies the following formula:

[0073] k x0 = k xt0 + k xr0

[0074] k z0 = k zt0 + k zr0

[0075]

[0076] (k x0 , k z0) The formula satisfied by the stationary points in POSP shows the spatial-frequency domain coupling relationship of the stationary points in POSP. Only when the stationary points of POSP exist, its asymptotic integral is not zero, corresponding to the impulse function term in the result formula for solving the above integrals with respect to x and z using POSP. (k x0 ,k z0 ) The formula satisfied by the stationary points in POSP also represents the relationship between the effective position of the local spectrum of the SAR image, the spatial position, the MIMO array, and the sampling frequency. Further, from the result formula for solving the above integrals with respect to x and z using POSP, it can be seen that the weight distribution of the local spectrum of the SAR image consists of two parts. One is the amplitude term f0α(θ t0 )α(θ r0 ) / (R t0 R r0 ) in the result formula for solving the above integrals with respect to x and z using POSP, and the other is the density of the spectral scatter point distribution determined by the formula (k x0 ,k z0 ) satisfied by the stationary points in POSP. That is, different transmit array elements, receive array elements, and sampling frequencies provide discrete distributed influences on the local spectra of the SAR image at different positions, and the final spectral weight is determined by the influence intensity and the discrete distribution density.

[0077] In some embodiments, as Figure 2 shown, the complete spectral range is divided into many polygon regions. Each polygon region contains only a single spectral discrete point, and the distance from any position within the region to this spectral discrete point is closer than to other spectral discrete points. Therefore, this region can be approximated as the effective influence region of this spectral discrete point. In addition, the points located at the edge of the spectral discrete point envelope lack boundary restrictions, which will cause the area of the region segmented by the Voronoi diagram method to be too large. Therefore, reasonable restrictions are needed. In the present invention, the theoretical spectral boundary of the complete dense MIMO array, that is, Figure 2 the boundary formed by the curve in

[0078] Finally, the weight distribution of the local spectrum of the SAR image can be expressed as:

[0079]

[0080] The complete expression of the local spectrum of the SAR image is:

[0081]

[0082] In the formula, β(k x ,k z)That is the area of the polygon region segmented by the Voronoi diagram method. It can be seen from the expression of the local spectrum of the SAR image that the weight distribution of the local spectrum of the SAR image at the position (x0, z0) directly affects the imaging performance of the SAR image for the target at (x0, z0).

[0083] In the embodiments of the present application, the spectral distribution and its weight of the SAR image can be accurately quantified through the calculation formula of the discrete spectral distribution and its weight, ensuring the accuracy and reliability of spectral analysis, so as to be able to accurately evaluate the imaging performance of the sparse MIMO array. The calculation process is also simplified by the Voronoi diagram method, thereby improving the efficiency of the optimization algorithm.

[0084] Optionally, in an embodiment of the present application, the calculation formula for the fitness of each particle is:

[0085]

[0086] where x and z are spatial variables, k x and k z are the spatial spectral wave number variables of the SAR image along the x-axis and z-axis directions respectively, is the weight distribution of the local spectrum of the SAR image, is the weight distribution of the local spectrum of the SISO array, ω(x, z), and ω(θ) are the attention weights of the imaging quality at different spatial positions.

[0087] In some embodiments, in order to evaluate whether the weight distribution of the local spectrum of the SAR image of the distributed sparse MIMO array has good imaging performance, under the same parameter conditions, the weight distribution of the local spectrum of the SISO array is calculated using the same method to evaluate its imaging performance through the degree of coincidence of the local spectrum weights. At the same time, in order to ensure that the distributed sparse MIMO array has good imaging performance at any position in space, the degrees of coincidence of the local spectra at each position in space need to be combined. Since the spectral support ranges of MIMO and SISO are inconsistent, in actual operation, the two spectral weights are not directly subtracted, but an approximation is adopted: considering that the size of the designed distributed MIMO single module is not large, the extra part of the spectrum of all MIMO modules compared with the SISO spectrum is small, and the angular ranges of the MIMO spectrum and the SISO spectrum are the same. Therefore, the two-dimensional spectral weight is integrated to obtain a one-dimensional spectrum with respect to the angular variable, and then subtracted. Finally, the method for quantifying the imaging performance of the distributed sparse MIMO array from the spectral angle is defined as:

[0088]

[0089] Where, ω(x,z) and ω(θ) represent the attention weights of the imaging quality at different spatial positions.

[0090] In the embodiments of the present application, the spectral weight difference between the sparse MIMO array and the SISO array can be accurately quantified through the fitness calculation formula of each particle, ensuring that the optimization process is directly targeted at the SAR image quality. By adjusting the contributions of different spatial positions through the attention weights, the imaging performance of the array can be accurately evaluated, and thus the optimization process has strong flexibility.

[0091] In step S103, based on the position of each particle and the global optimal fitness, calculate the initial temperature of the annealing algorithm and determine the global optimal position.

[0092] It can be understood that in the embodiments of the present application, the initial temperature of the annealing algorithm can be calculated based on the global optimal fitness. The global optimal position can refer to the position of the particle with the optimal fitness found in the entire search space, which can represent the best solution found in the current search process.

[0093] In the actual execution process, in the embodiments of the present application, the initial temperature of the annealing algorithm can be calculated based on the global optimal fitness, calculate the annealing algorithm fitness of each particle at the current temperature, and adopt the roulette wheel strategy to select particles according to the fitness value with probability, avoiding premature convergence of the algorithm. Select one from the individual optimal positions to replace the global optimal position to ensure that the algorithm can jump out of the local optimum during the search process.

[0094] In the embodiments of the present application, by calculating the initial temperature, it can be ensured that the algorithm has sufficient global search ability in the initial stage of the search, avoiding falling into the local optimum, and selecting the global optimal position through the roulette wheel strategy to enhance the escape ability of the algorithm from local traps.

[0095] In step S104, based on the initial temperature of the annealing algorithm and determining the global optimal position, update the velocity of each particle and calculate the new fitness to perform the annealing operation until the iteration termination condition is met, generating an optimized single-module sparse MIMO array.

[0096] In the actual execution process, in the embodiments of the present application, the velocity of each particle can be updated according to the particle swarm formula, the fitness of each particle is calculated again, and the optimal position of each particle and the optimal position of the population are updated. In addition, when each particle is updated, the constraint conditions need to be checked. For example, in order to make the antenna pattern of the designed antenna as ideal as possible, it is required that the spacing between the antennas must be greater than 4 mm, that is, the constraint condition in the optimization process.

[0097] Further, an annealing operation is performed to determine whether the set termination condition is met. If it is met, it indicates the end of optimization; if not, return to step S103 to calculate the fitness of each particle under the current temperature by the annealing algorithm, and select the global optimal position, and iterate cyclically.

[0098] In the embodiment of the present application, by updating the velocity and position of the particles and combining the temperature annealing mechanism of the annealing algorithm, the local optimal solution can be jumped out during the search process, and quickly converge to the global optimal solution, thereby improving the global search ability and convergence efficiency of the algorithm, and then significantly improving the imaging performance of the sparse MIMO array, and effectively suppressing the grating lobe phenomenon in near-field SAR imaging.

[0099] Optionally, in an embodiment of the present application, the iteration termination condition is that the number of loops reaches a preset maximum number of iterations or the objective function value is lower than a preset threshold.

[0100] It can be understood that the preset threshold in the embodiment of the present application can be set by those skilled in the art according to the actual situation, and no specific limitation is made here.

[0101] For example, in the embodiment of the present application, when the number of loops reaches the preset maximum number of iterations, or when the objective function value to be optimized is lower than a certain threshold, it indicates that the optimization has converged, which is the set termination condition. This optimization algorithm can quickly converge the objective function to the global optimal solution and avoid falling into local traps during the optimization process. For example, the designed single-module sparse MIMO array after optimization is as Figure 3 shown, and the distributed sparse MIMO array composed of 8 single-module sparse MIMO arrays is as Figure 4 shown.

[0102] In the embodiment of the present application, by setting the threshold of the objective function value, overfitting during the optimization process of the algorithm can be avoided, and the stability of the optimization result can be ensured.

[0103] The optimization method for the distributed sparse MIMO array structure design applicable to near-field imaging proposed according to the embodiments of the present application can obtain the initial value of each particle based on the distribution characteristics and size of the sparse MIMO array, obtain the preliminary structure of the sparse MIMO array according to the initial value of each particle, and based on the preliminary structure, use the degree of coincidence between the spatial frequency spectrum weight distribution of the SAR image and the ideal weight distribution as the optimization target to determine the position of each particle and the global optimal fitness, and combine the SA-PSO algorithm to determine the global optimal position, thereby realizing the optimized design of the distributed sparse MIMO array topology structure and overcoming the deficiencies of traditional methods in dealing with space-variant characteristics. Thus, it solves the problems in the related technologies, such as the large deviation of the far-field approximation method in near-field imaging, the severe space-variant characteristics of the near-field SAR image resulting in the mismatch between the imaging performance of the virtual array and the MIMO array, leading to obvious grating lobes, and the PSO algorithm is prone to falling into the local optimal solution when facing the non-linear multi-dimensional objective function, resulting in the degradation of the SAR image performance, etc.

[0104] Next, a device for optimizing the design of a distributed sparse MIMO array structure applicable to near-field imaging proposed according to the embodiments of the present application will be described with reference to the accompanying drawings.

[0105] Figure 5 It is a block diagram of a device for optimizing the design of a distributed sparse MIMO array structure applicable to near-field imaging according to the embodiments of the present application.

[0106] As Figure 5 shown, the device 10 for optimizing the design of a distributed sparse MIMO array structure applicable to near-field imaging includes: an initialization module 100, a determination module 200, a calculation module 300, and a generation module 400.

[0107] Among them, the initialization module 100 is used to determine at least one variable to be optimized based on the distribution characteristics and size of the sparse MIMO array, so as to initialize the position and velocity, attention weight, learning factor, and annealing velocity of the particle swarm corresponding to at least one variable to be optimized, and obtain the initial value of each particle.

[0108] The determination module 200 is used to obtain the preliminary structure of the sparse MIMO array according to the initial value of each particle, and based on the preliminary structure, combine the Voronoi diagram method with the effective weight distribution optimization objective function of the spatial frequency spectrum, calculate the fitness of each particle according to the objective function, and determine the position of each particle and the global optimal fitness.

[0109] The calculation module 300 is used to calculate the initial temperature of the annealing algorithm and determine the global optimal position based on the position of each particle and the global optimal fitness.

[0110] A generation module 400, configured to determine a global optimal position based on an initial temperature of an annealing algorithm, update the velocity of each particle, and calculate a new fitness value to perform an annealing operation until an iteration termination condition is met, so as to generate an optimized single-module sparse MIMO array.

[0111] Optionally, in an embodiment of the present application, the determination module 200 includes: a conversion unit and a recording unit.

[0112] The conversion unit is configured to quantify the discrete spectrum distribution and its weight at different positions in the imaging space, so as to calculate the local spectrum weight distribution of the SISO array, and convert the two-dimensional spectrum weights of the sparse MIMO array and the SISO array into one-dimensional spectra with respect to the angular variable, so as to obtain an optimized objective function.

[0113] The recording unit is configured to calculate the fitness of each particle based on the objective function, and record the position, global optimal position, fitness, and global optimal fitness of each particle.

[0114] Optionally, in an embodiment of the present application, the calculation formula for the discrete spectrum distribution and its weight is:

[0115]

[0116] where k x , k z are the spatial spectrum wave number variables of the SAR image along the x-axis and z-axis directions respectively, that is, the corresponding items in the spatial wave number domain of x and z, R t , R r are the distances between the transmitting and receiving array elements and the spatial target point respectively, θ t , θ r are the azimuth angles of the spatial target point relative to the transmitting and receiving array elements respectively, θ t0 , θ r0 , R t0 , R r0 are the θ t , θ r , R t , R r variables corresponding to the point scatterer (x0, z0), f0 is the reflectivity, α(·) is the radiation pattern of the transmitting and receiving antennas, and β(k x , k z ) is the area of the polygon region divided by the Voronoi diagram method.

[0117] Optionally, in an embodiment of the present application, the calculation formula for the fitness of each particle is:

[0118]

[0119] where x and z are spatial variables, and k x and k z are the spatial spectrum wavenumber variables of the SAR image along the x - axis and z - axis directions respectively, is the weight distribution of the local spectrum of the SAR image, is the local spectrum weight distribution of the SISO array, and ω(x,z), ω(θ) are the attention weight of the imaging quality at different spatial positions.

[0120] Optionally, in an embodiment of the present application, the iteration termination condition is that the number of loops reaches a preset maximum number of iterations or the objective function value is lower than a preset threshold.

[0121] It should be noted that the foregoing explanation of the embodiment of the distributed sparse MIMO array structure design optimization method is also applicable to the distributed sparse MIMO array structure design optimization device for near - field imaging in this embodiment, and will not be elaborated here.

[0122] The distributed sparse MIMO array structure design optimization device for near - field imaging proposed according to the embodiments of the present application can obtain the initial value of each particle based on the distribution characteristics and size of the sparse MIMO array, obtain the preliminary structure of the sparse MIMO array according to the initial value of each particle, and based on the preliminary structure, use the degree of coincidence between the spatial frequency spectrum weight distribution of the SAR image and the ideal weight distribution as the optimization goal to determine the position of each particle and the global optimal fitness, and combine the SA - PSO algorithm to determine the global optimal position, thereby realizing the optimized design of the distributed sparse MIMO array topology structure and overcoming the deficiencies of traditional methods in dealing with space - variant characteristics. Thus, it solves the problems in the related art, such as the large deviation of the far - field approximation method in near - field imaging, the severe space - variant characteristics of the near - field SAR image resulting in the mismatch between the imaging performance of the virtual array and the MIMO array, leading to obvious grating lobes, and the PSO algorithm is prone to falling into a local optimal solution when facing a non - linear multi - dimensional objective function, resulting in a decline in the performance of the SAR image.

[0123] Figure 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include:

[0124] a memory 601, a processor 602, and a computer program stored in the memory 601 and executable on the processor 602.

[0125] When the processor 602 executes the program, it implements the distributed sparse MIMO array structure design method for near - field imaging provided in the above - mentioned embodiment.

[0126] Furthermore, the electronic device further includes:

[0127] A communication interface 603 for communication between the memory 601 and the processor 602.

[0128] A memory 601 for storing computer programs that can run on the processor 602.

[0129] The memory 601 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk memory.

[0130] If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0131] Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a single chip, the memory 601, the processor 602, and the communication interface 603 can communicate with each other through an internal interface.

[0132] The processor 602 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0133] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above-described distributed sparse MIMO array structure design method applicable to near-field imaging.

[0134] The embodiments of the present application also provide a computer program, on which a computer program is stored, and when the program is executed by a processor, it implements the above-described distributed sparse MIMO array structure design method applicable to near-field imaging.

[0135] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0136] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0137] Any process or method description shown in the flowchart or described in other ways herein may be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0138] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection part (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0139] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented by a combination of any one or more of the following techniques known in the art: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), and the like.

[0140] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above-described embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0141] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0142] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A design optimization method for a distributed sparse MIMO array structure applicable to near-field imaging, characterized in that Including the following steps: Based on at least one variable to be optimized determined according to the distribution characteristics and size of a sparse multiple-input multiple-output (MIMO) array, initialize the positions and velocities, attention weights, learning factors, and annealing speeds of the particle swarms corresponding to the at least one variable to be optimized, to obtain the initial values of each particle; Obtain the preliminary structure of the sparse MIMO array according to the initial value of each particle, and based on the preliminary structure, combine the Voronoi diagram method with the effective weight distribution of the spatial frequency spectrum to optimize the objective function, calculate the fitness of each particle according to the objective function, and determine the position and global optimal fitness of each particle; Based on the position of each particle and the global optimal fitness, calculate the initial temperature of the annealing algorithm and determine the global optimal position; Based on the initial temperature of the annealing algorithm and the determined global optimal position, update the velocity of each particle and calculate the new fitness to perform the annealing operation until the iteration termination condition is met, to generate an optimized single-module sparse MIMO array.

2. The method according to claim 1, wherein The step of calculating the fitness of each particle according to the objective function and determining the position and global optimal position of each particle based on the preliminary structure includes: Quantize the discrete spectrum distribution and its weights at different positions in the imaging space to calculate the local spectrum weight distribution of a single-input single-output (SISO) array, and respectively convert the two-dimensional spectrum weights of the sparse MIMO array and the SISO array into one-dimensional spectra with respect to the angular variable to obtain the optimized objective function; Based on the objective function, calculate the fitness of each particle, and record the position, global optimal position, fitness, and global optimal fitness of each particle.

3. The method according to claim 2, wherein The calculation formula for the discrete spectrum distribution and its weights is: where k x and k z are the spatial spectral wavenumber variables of the SAR image along the x- and z-axis directions, respectively, R t and R r are the distances between the transmitting and receiving array elements and the spatial target point, respectively, θ t and θ r are the azimuth angles of the spatial target point with respect to the transmitting and receiving array elements, respectively, θ t0 and θ r0 , R t0 and R r0 are the θ t , θ r , R t , and R r variables corresponding to the point scatterer (x0, z0), f0 is the reflectivity, α(·) is the radiation pattern of the transmitting and receiving antennas, and β(k x , k z ) is the area of the polygon region segmented by the Voronoi diagram method.

4. The method according to claim 2, wherein The calculation formula for the fitness of each particle is: where x and z are spatial variables, and k x and k z are the spatial spectral wavenumber variables of the SAR image along the x- and z-axis directions, respectively, is the weight distribution of the local spectrum of the SAR image, is the local spectrum weight distribution of the SISO array, and ω(x, z) and ω(θ) are the attention weight of the imaging quality at different spatial positions.

5. The method according to any one of claims 1-4, characterized in that, The iteration termination condition is that the number of loops reaches the preset maximum number of iterations or the objective function value is lower than the preset threshold.

6. A device for optimizing the design of a distributed sparse MIMO array structure suitable for near-field imaging, characterized in that Including: An initialization module, configured to initialize the positions and velocities, attention weights, learning factors, and annealing speeds of the particle swarms corresponding to at least one variable to be optimized determined according to the distribution characteristics and size of a sparse MIMO array, to obtain the initial values of each particle; A determination module, configured to obtain the preliminary structure of the sparse MIMO array according to the initial value of each particle, and based on the preliminary structure, combine the Voronoi diagram method with the effective weight distribution of the spatial frequency spectrum to optimize the objective function, calculate the fitness of each particle according to the objective function, and determine the position and global optimal fitness of each particle; A calculation module, configured to calculate the initial temperature of the annealing algorithm and determine the global optimal position based on the position of each particle and the global optimal fitness; An optimization module, configured to update the velocity of each particle and calculate the new fitness based on the initial temperature of the annealing algorithm and the determined global optimal position to perform the annealing operation until the iteration termination condition is met, to generate an optimized single-module sparse MIMO array.

7. The device according to claim 6, characterized in that, The determination module includes: A conversion unit, configured to quantify the discrete spectrum distribution and its weights at different positions in the imaging space, so as to calculate the local spectrum weight distribution of the SISO array, and convert the two-dimensional spectrum weights of the sparse MIMO array and the SISO array into one-dimensional spectra with respect to the angular variable, so as to obtain an optimized objective function; A recording unit, configured to calculate the fitness of each particle based on the objective function, and record the position, global optimal position, fitness and global optimal fitness of each particle.

8. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for designing a distributed sparse MIMO array structure applicable to near-field imaging according to any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method for designing a distributed sparse MIMO array structure applicable to near-field imaging according to any one of claims 1-5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed to implement the method for designing a distributed sparse MIMO array structure applicable to near-field imaging according to any one of claims 1-5.

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