A distributed array layout structure optimization method, system and storage medium based on discrete quantum electromagnetic field optimization mechanism

Abstract

The present invention provides a distributed array layout structure optimization method, system and storage medium based on a discrete quantum electromagnetic field optimization mechanism, which relates to the technical field of array signal processing, and aims to solve the problem that the existing methods mostly use the maximum sidelobe level as the optimization target, and the performance of the distributed array is seriously attenuated under harsh conditions such as small sampling snapshots and impact noise. The method includes the following processes: setting auxiliary source information and noise information in space to simulate and test the distributed array, and taking the source azimuth mean square error as the optimization target equation; initializing the quantum electromagnetic particle information in the quantum electromagnetic field to obtain the local and global optimal measurement positions; respectively using an evolution strategy based on quantum coding and an evolution strategy based on quantum rotation gate to update the quantum rotation angle and quantum position of each electromagnetic particle and obtain the measurement position; updating the local and global optimal measurement positions of the particles; and converting the final global optimal solution into the required array layout structure information.

Description

Technical Field

[0001] The present invention relates to the technical field of array signal processing, and in particular to a distributed array layout structure optimization method, system and storage medium based on a discrete quantum electromagnetic field optimization mechanism. Background Art

[0002] Distributed arrays, as an important branch of antenna arrays, are widely used today. By using multiple small-aperture array antennas to form a distributed array, the performance of the system can be further improved without increasing the number of system elements. At the same time, it is more adaptable to certain specific application scenarios and has higher flexibility. However, in the current distributed array system, there is no good method to determine the position arrangement of each sub-array. It often relies on experience and adopts an evenly spaced arrangement. However, different arrangements will directly affect the direction finding accuracy and performance of the distributed array, and the current distributed array does not perform well under harsh conditions such as small snapshots. Therefore, it is of great significance to study a distributed array layout structure that can maintain high performance under harsh conditions such as small sampling snapshots and impact noise environments.

[0003] At present, most of the optimization of distributed array layout is based on the perspective of array sparsity. By optimizing the maximum sidelobe level in the distributed array pattern, the anti-interference ability of the array during beamforming is improved. However, this method does not improve the performance of the direction of arrival estimation much, because the factors that affect the direction-finding performance of the distributed array are not only the maximum sidelobe level of the array. Many factors such as the number of sampling snapshots and the signal-noise environment in actual applications will have a certain impact on the direction-finding performance of the array. Moreover, as the application scenarios become more complex and diverse, many scenarios such as drone formations and surface unmanned ships may involve the technology of distributed array direction of arrival estimation. Therefore, it is of great significance to improve the performance of the direction of arrival estimation of distributed arrays, especially under some extreme conditions, such as extremely small sampling snapshots and impact noise.

[0004] After searching the existing literature, it was found that Rocca P, Mailloux RJ, Toso G et al. published "GA-Based Optimization of Irregular Subarray Layouts for Wideband Phased Arrays Design" in "IEEE Antennas and Wireless Propagation Letters" (2015, 14: 131-134.), which proposed to use genetic algorithm to optimize the position of array elements, but its application scenario only considers the small aperture array model under relatively ideal environment, and whether it still has good effect on the distributed array structure under complex conditions remains to be studied. An Zhengshuai et al. published "Research on Distributed Array Grating Lobe Suppression Algorithm Based on Improved Genetic Algorithm" in "Fire Control Radar Technology", which mentioned that by analyzing the topological structure of distributed array antennas, and then using improved genetic algorithms to optimize the subarray positions of distributed arrays, the effect of suppressing grating lobes can be achieved, but its goal is also to simply optimize the sidelobe level, and the improvement of the direction finding performance of actual distributed arrays is not obvious. Ang CW, See CM, Kot AC et al. published "Optimization of array geometry for identifiable high resolution parameter estimation insensor array signal processing" in the IEEE International Conference on Information. They proposed using evolutionary algorithms to optimize the design of array structures of any size and configuration, and simulations proved that the optimal array geometry obtained is better than the traditional circular array, but the improvement is not obvious compared to the more stable uniform square array, and the optimization effect of the distributed array system needs further research. It can be seen that the existing distributed array layout optimization method mainly takes the maximum sidelobe level as the optimization target, and the performance improvement for the direction of arrival estimation is not high. Especially under harsh conditions such as small sampling snapshots and impulse noise, the problem of serious performance degradation of existing distributed arrays needs to be solved urgently. Summary of the invention

[0005] The technical problems to be solved by the present invention are:

[0006] Existing distributed array layout optimization methods mostly use the maximum sidelobe level as the optimization target, which does not significantly improve the performance of direction of arrival estimation. Especially under harsh conditions such as small sampling snapshots and impulse noise, the performance of existing distributed arrays is severely degraded.

[0007] The present invention adopts the following technical solutions to solve the above technical problems:

[0008] The present invention provides a distributed array layout structure optimization method based on a discrete quantum electromagnetic field optimization mechanism, comprising the following steps:

[0009] Step 1: Set the auxiliary source information and noise information in space to simulate the direction finding performance of the test distributed array in actual applications, and use the calculated source azimuth mean square error as the optimization target equation;

[0010] Step 2: Initialize the quantum electromagnetic particle information in the quantum electromagnetic field to obtain the local optimal measurement position set and the global optimal measurement position;

[0011] Step 3: Use an evolutionary strategy based on a quantum rotation gate to update the quantum rotation angle and quantum position of each electromagnetic particle and obtain the measured position;

[0012] Step 4: Use the evolution strategy 2 based on the quantum rotation gate to update the quantum rotation angle and quantum position of the updated electromagnetic particle again and obtain the measured position;

[0013] Step 5: Update the local optimal measurement position and the global optimal measurement position of the particle;

[0014] Step 6: Determine whether the termination iteration number T has been reached. If so, output the global optimal measurement position Otherwise, let t = t + 1, repeat steps 3 to 5, and continue iterating;

[0015] Step 7: Convert the output global optimal solution into the required array layout structure information.

[0016] Furthermore, step 1 includes the following steps:

[0017] Step 1-1: Arrange N narrow-band far-field auxiliary signal sources with known positions and wavelengths in space, where The azimuth of the source is Set the noise in the space to impact noise, and set the necessary parameters of the distributed array to be optimized;

[0018] Step 1-2: For any given distributed array layout structure The distributed array is placed in a space where auxiliary information sources and corresponding noise environments are set up. At this time, the mathematical model of the l-th snapshot sampling data received by the array is B(l)=A(θ)s(l)+n(l), where l=1,2,…,L, L is the maximum number of snapshots, and B(l)=[B1(l),B2(l),…,B M (l)] Tis the lth snapshot data vector of M×1 dimension received by the array, the superscript T indicates transposition, s(l) is the N×1 dimensional signal vector, n(l) is the M×1 dimensional impulse noise vector obeying the SαS stable distribution, A(θ)=[a(θ1),a(θ2),…,a(θ N )] is an M×N dimensional array flow matrix, where θ = [θ1,θ2,…,θ N ]The azimuth vector of the source, is the first A steering vector, The distributed array as a whole takes the origin as the reference point, and sets the position of the first array element as the origin. is the position of the mth array element;

[0019] Step 1-3: Calculate the estimated mean square error of the signal source as the optimization target to evaluate the direction finding performance of the distributed array. The target equation to be optimized is: in, is the estimated mean square error of the source, is the potential solution of any given distributed array position structure, N is the number of known sources in space, For the The accurate azimuth of the auxiliary signal source, By obedience The azimuth angle estimated by the ranging distribution array,

[0020] Furthermore, the necessary parameters of the distributed array to be optimized in step 1-2 are set, including: specifying that the total number of array elements of the distributed system is M, the number of array elements of each subarray is G, a uniform linear array arrangement is adopted, and the array element spacing in the subarray is The total number of subarrays is Z, M = G × Z. The arrangement principle of each subarray in space refers to the one-dimensional arrangement as a whole, and the spacing d between each subarray h The array element spacing is An integer multiple of h=1,…,Z-1; therefore, the spacing vector between sub-arrays is d=[d1,d2,…,d Z-1 ], which is the layout structure information of the distributed array to be optimized, which can be further simplified to obtain

[0021] Furthermore, step 1-2 also includes performing a de-impact process on the received data, specifically:

[0022] First construct the fractional low-order matrix The processing method is that the element in row i and column j for In the formula, B i(l) and B j (l) respectively represent the first and second snapshot signal data vectors received by the array. Peace dimension, l=1,2,…,L,1≤i≤M,1≤j≤M,(·) * represents the conjugate operation, τ is the low-order moment order, 1≤τ≤α, α is the impulse noise characteristic index;

[0023] Then, the logarithmic low-order covariance matrix Further processing, its features are decomposed into in, It is a signal subspace spanned by the eigenvectors corresponding to N large eigenvalues. is a diagonal matrix of corresponding large eigenvalues, is the noise subspace spanned by the eigenvectors corresponding to the remaining smaller eigenvalues, Is a diagonal matrix composed of the remaining smaller eigenvalues; get the spatial spectrum equation to be searched

[0024] Finally, by searching the spectrum peak, we find the angle vectors corresponding to the N maximum values ​​of the constructed space spectrum equation. This is the distributed array layout The estimated source wave direction is given below.

[0025] Furthermore, step 2 improves the continuous electromagnetic field optimization mechanism into a discrete quantum electromagnetic field optimization mechanism, including the following steps:

[0026] Step 2-1: First, assume that there are K quantum electromagnetic particles in the electromagnetic field. Each electromagnetic particle has its own quantum position, and the initial value of its quantum position is randomly generated between [0,1]. Then the quantum position of the kth quantum electromagnetic particle of the tth generation is q=1,2,…,Q, and then the corresponding measurement position of the particle is obtained by measurement The measurement equation is in, is a random number that follows a uniform distribution between [0,1]. Q is the maximum dimension of the solution space;

[0027] Step 2-2: First, the Q-bit binary number Per The form of a group of bits is converted into a Z-1 decimal integer array, which is the potential solution of the array layout structure corresponding to the kth quantum electromagnetic particle of the tth generation. Finally, the distance vector obtained after transformation Substitute the fitness function Obtain the fitness value of the kth quantum electromagnetic particle of the tth generation;

[0028] Step 2-3: Record the optimal measurement position of the k-th quantum electromagnetic particle after the t-th generation optimization search as the local optimal position The measurement position of the entire electromagnetic field with the best fitness value up to the current time is recorded as the global optimal position Set the number of termination iterations to T.

[0029] Further, step 3 includes the following steps:

[0030] First, set a selection probability ρ, and then generate a random number between [0,1] that follows a uniform distribution If it is less than the selection probability ρ, the q-th quantum rotation angle of the k-th quantum electromagnetic particle of the t+1th generation is defined as If it is greater than or equal to the selection probability ρ, then the q-th quantum rotation angle of the k-th quantum electromagnetic particle is defined as Among them, r1, r2, r3, r4 and r5 are random numbers that are uniformly distributed between [0,1]. is the proportionality coefficient, is the qth dimension of an arbitrarily selected positively polarized particle of the tth generation labeled β, is the qth dimension of an arbitrarily selected negatively polarized particle of the tth generation labeled ζ, The tth generation number is arbitrarily selected as The qth dimension of a neutral particle, is the qth dimension of the kth local optimal measurement position of the tth generation, is the qth dimension of the tth generation global optimal measurement position,

[0031] Then the evolution of the q-dimensional quantum position of the k-th electromagnetic particle of the t+1th generation is defined as in, is a random number that follows a uniform distribution between [0,1], c1 represents the probability of performing a reversal operation on the quantum bit when the quantum rotation angle is 0, and its value is a constant between [0,1 / Q];

[0032] Finally, the quantum position is measured to obtain the corresponding measurement position The measurement equation is in, is a random number that follows a uniform distribution between [0,1], q=1,2,…,Q.

[0033] Further, step 4 includes the following steps:

[0034] The quadratic update equation for the qth-dimensional quantum rotation angle of the kth quantum electromagnetic particle is defined as Among them, u1 and u2 are random numbers that obey the standard Gaussian distribution. represents the qth dimension of the average of the K local optimal measurement positions in the tth generation, q = 1, 2, ..., Q, is the qth dimension of the global optimal measurement position of the tth generation; the evolution equation of the qth dimension quantum position of the kth quantum electromagnetic particle of the t+1th generation is defined as in, is a random number uniformly distributed between [0,1]; then the measured position The measurement equation is in, is a random number that follows a uniform distribution between [0,1], q=1,2,…,Q.

[0035] Further, step 5 includes the following steps:

[0036] The measured position of the kth particle after update Convert to distance vector Substitute the fitness function to obtain the fitness value of the kth particle measurement position; then update the local optimal measurement position of the kth particle to determine whether the updated fitness value satisfies If it is satisfied, then the local optimal measurement position of the kth particle of the t+1th generation is updated to Otherwise, the local optimal measurement position of the t+1th generation remains unchanged from the previous generation. Finally, the global optimal measurement position is updated and the updated local optimal measurement position is The one with the smallest fitness value Denoted as the global optimal measurement position of the t+1th generation quantum electromagnetic particle

[0037] A distributed array layout structure optimization system based on a discrete quantum electromagnetic field optimization mechanism, the system having a program module corresponding to the steps of any one of the above technical solutions, and executing the steps of the above distributed array layout structure optimization method based on a discrete quantum electromagnetic field optimization mechanism during operation.

[0038] A computer-readable storage medium stores a computer program, wherein the computer program is configured to implement the steps in the distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism described in any one of the above technical solutions when called by a processor.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. The present invention combines quantum optimization theory with electromagnetic field optimization mechanism. By designing a new evolution strategy based on quantum coding and quantum revolving gate, on the one hand, it optimizes its update method, reduces parameter settings, makes the search mechanism more concise, and reduces the impact of improper parameter settings on the performance of the search mechanism. On the other hand, it makes up for the shortcomings of the original search mechanism when facing discrete optimization problems, and greatly improves its ability to handle discrete problems.

[0041] 2. The present invention directly starts from the direction finding results, takes the direction finding accuracy and root mean square error of the array as optimization targets, and infers the array layout structure from the results, thereby avoiding the problem of insufficient consideration of influencing factors in array optimization; it not only improves the direction finding accuracy of the distributed array system, but also solves the problem of serious performance degradation of the distributed array under small sampling snapshots, thereby greatly improving the performance of the optimized distributed array system.

[0042] 3. The present invention breaks through the application limitation of distributed array systems with serious performance degradation under conditions of small sampling snapshots, impact noise, etc., and obtains a distributed array layout structure with a wider adaptability, wider applicable conditions and higher performance. It provides new ideas for the optimized design of distributed array layout, improves the direction finding accuracy and robustness, and achieves the goal of high-performance and stable direction finding under small snapshots. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flow chart of a distributed array layout structure optimization method based on a discrete quantum electromagnetic field optimization mechanism in an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of distributed array sub-array layout optimization in an embodiment of the present invention;

[0045] Figure 3 is a curve showing the relationship between the number of snapshots and the estimated mean square error under different array structures in an embodiment of the present invention;

[0046] Figure 4 is a curve showing the relationship between the number of snapshots and the estimated success probability under different array structures in an embodiment of the present invention;

[0047] Figure 5 1 is a relationship curve between the generalized signal-to-noise ratio and the estimation success probability under different array structures in the embodiment of the present invention. DETAILED DESCRIPTION

[0048] In the description of the present invention, it should be noted that the terms "first", "second", and "third" mentioned in the embodiments of the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0050] Specific implementation plan 1: Figure 1 As shown, the present invention provides a distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism, comprising the following steps:

[0051] Step 1: Set the auxiliary source information and noise information in space to simulate the direction finding performance of the test distributed array in actual applications, and use the calculated source azimuth mean square error as the optimization target equation;

[0052] Step 2: Initialize the quantum electromagnetic particle information in the quantum electromagnetic field to obtain the local optimal measurement position set and the global optimal measurement position;

[0053] Step 3: Use an evolutionary strategy based on a quantum rotation gate to update the quantum rotation angle and quantum position of each electromagnetic particle and obtain the measured position;

[0054] Step 4: Use the evolution strategy 2 based on the quantum rotation gate to update the quantum rotation angle and quantum position of the updated electromagnetic particle again and obtain the measured position;

[0055] Step 5: Update the local optimal measurement position and the global optimal measurement position of the particle;

[0056] Step 6: Determine whether the termination iteration number T has been reached. If so, output the global optimal measurement position Otherwise, let t = t + 1, repeat steps 3 to 5, and continue iterating;

[0057] Step 7: Convert the output global optimal solution into the required array layout structure information.

[0058] This implementation scheme is different from the conventional method of taking the maximum sidelobe level of the array as the optimization target. Instead, it focuses directly on the accuracy and performance of the distributed array in the direction of arrival estimation, and takes the estimated mean square error of the source in the actual array application as the optimization target. On the one hand, it improves the problem of severe performance degradation of the distributed array under certain extreme conditions, and on the other hand, it improves the direction-finding performance of the distributed array system. Then, by using quantum optimization theory to innovate the design and improve the classical electromagnetic field optimization mechanism, using quantum coding and quantum evolution mechanisms, it makes up for the lack of ability of the electromagnetic field optimization mechanism in discrete optimization problems. Finally, with the help of the designed discrete quantum electromagnetic field optimization mechanism, the optimal subarray position structure is searched, which not only reduces the computational complexity of the optimization problem, but also further ensures the superiority of the obtained subarray position layout.

[0059] Specific implementation plan 2: Step 1 includes the following steps:

[0060] This embodiment simulates and tests the direction finding performance of the corresponding distributed array in the application by setting known signal sources in multiple spaces, and uses the estimated signal source azimuth mean square error as the final objective equation of the optimization mechanism.

[0061] Step 1-1: Arrange N narrow-band far-field auxiliary signal sources with known positions and wavelengths in space, where The azimuth of the source is Set the noise in the space to impact noise, and set the necessary parameters of the distributed array to be optimized;

[0062] Because the implementation method is to take the specific performance of the distributed array during direction finding, that is, the estimated mean square error of the signal source obtained after direction finding as the optimization target, the smaller the estimated mean square error of the signal source is, the higher the direction finding performance of the distributed array is. Therefore, the distributed array layout structure corresponding to the minimum estimated mean square error searched is the target structure.

[0063] Step 1-2: For any given distributed array layout structure The distributed array is placed in a space where auxiliary information sources and corresponding noise environments are set up. At this time, the mathematical model of the l-th snapshot sampling data received by the array is B(l)=A(θ)s(l)+n(l), where l=1,2,…,L, L is the maximum number of snapshots, and B(l)=[B1(l),B2(l),…,B M (l)] T is the lth snapshot data vector of M×1 dimension received by the array, the superscript T indicates transposition, s(l) is the N×1 dimensional signal vector, n(l) is the M×1 dimensional impulse noise vector obeying the SαS stable distribution, A(θ)=[a(θ1),a(θ2),…,a(θ N)] is an M×N dimensional array flow matrix, where θ = [θ1,θ2,…,θ N ]The azimuth vector of the source, is the first A steering vector, The distributed array as a whole takes the origin as the reference point, and sets the position of the first array element as the origin. is the position of the mth array element;

[0064] Step 1-3: Calculate the estimated mean square error of the signal source as the optimization target to evaluate the direction finding performance of the distributed array. The target equation to be optimized is: in, is the estimated mean square error of the source, is the potential solution of any given distributed array position structure, N is the number of known sources in space, For the The accurate azimuth of the auxiliary signal source, By obedience The azimuth angle estimated by the ranging distribution array, The rest of this embodiment is the same as the first specific embodiment.

[0065] Specific implementation plan three: The necessary parameters of the distributed array to be optimized as described in step 1-2 are set, including: specifying that the total number of array elements of the distributed system is M, the number of array elements of each sub-array is G, and the uniform linear array arrangement is adopted, and the array element spacing in the sub-array is The total number of subarrays is Z, M = G × Z. The arrangement principle of each subarray in space refers to the one-dimensional arrangement as a whole, and the spacing d between each subarray h The array element spacing is An integer multiple of h=1,…,Z-1; therefore, the spacing vector between sub-arrays is d=[d1,d2,…,d Z-1 ], which is the layout structure information of the distributed array to be optimized, which can be further simplified to obtain The rest of this implementation plan is the same as the second specific implementation plan.

[0066] Specific implementation scheme 4: Due to the existence of impulse noise, the received data needs to be subjected to impulse removal processing. Therefore, step 1-2 also includes the need to perform impulse removal processing on the received data, specifically:

[0067] First construct the fractional low-order matrix The processing method is that the element in row i and column j for In the formula, B i (l) and B j(l) respectively represent the first and second snapshot signal data vectors received by the array. Peace dimension, l=1,2,…,L,1≤i≤M,1≤j≤M,(·) * represents the conjugate operation, τ is the low-order moment order, 1≤τ≤α, α is the impulse noise characteristic index;

[0068] Then, the logarithmic low-order covariance matrix Further processing, its features are decomposed into in, It is a signal subspace spanned by the eigenvectors corresponding to N large eigenvalues. is a diagonal matrix of corresponding large eigenvalues, is the noise subspace spanned by the eigenvectors corresponding to the remaining smaller eigenvalues, Is a diagonal matrix composed of the remaining smaller eigenvalues; get the spatial spectrum equation to be searched

[0069] Finally, by searching the spectrum peak, we find the angle vectors corresponding to the N maximum values ​​of the constructed space spectrum equation. This is the distributed array layout The rest of this implementation is the same as the second implementation.

[0070] Specific implementation plan 5: Step 2: Improve the continuous electromagnetic field optimization mechanism to a discrete quantum electromagnetic field optimization mechanism, including the following steps:

[0071] Step 2-1: Since the parameter to be optimized is an integer vector Therefore, in order to solve this type of discrete integer optimization problem, the original continuous electromagnetic field optimization mechanism is improved and designed as a discrete quantum electromagnetic field optimization mechanism. First, assume that there are K quantum electromagnetic particles in the electromagnetic field, each of which has its own quantum position, and the initial value of its quantum position is randomly generated between [0,1]. Then the quantum position of the kth quantum electromagnetic particle of the tth generation is q=1,2,…,Q, and then the corresponding measurement position of the particle is obtained by measurement The measurement equation is in, is a random number that follows a uniform distribution between [0,1]. Q is the maximum dimension of the solution space;

[0072] Step 2-2: First, the Q-bit binary number Per The form of a group of bits is converted into a Z-1 decimal integer array, which is the potential solution of the array layout structure corresponding to the kth quantum electromagnetic particle of the tth generation. Finally, the distance vector obtained after transformation Substitute the fitness function Obtain the fitness value of the kth quantum electromagnetic particle of the tth generation;

[0073] Step 2-3: Record the optimal measurement position of the k-th quantum electromagnetic particle after the t-th generation optimization search as the local optimal position The measurement position of the entire electromagnetic field with the best fitness value up to the current time is recorded as the global optimal position Set the termination iteration number to T. According to the characteristics of the actual electromagnetic field, refer to the fitness value of each particle, and divide the polarity of the quantum electromagnetic particles in the electromagnetic field into three types according to the size of the fitness value: positive polarity, neutral polarity and negative polarity, with the proportions being and After the initialization of the discrete quantum electromagnetic field optimization mechanism is completed, the positions of the electromagnetic particles are updated using two quantum update strategies. The rest of this implementation is the same as the first implementation.

[0074] Specific implementation plan six: Step 3 includes the following steps:

[0075] First, set a selection probability ρ, and then generate a random number between [0,1] that follows a uniform distribution If it is less than the selection probability ρ, the q-th quantum rotation angle of the k-th quantum electromagnetic particle of the t+1th generation is defined as If it is greater than or equal to the selection probability ρ, then the q-th quantum rotation angle of the k-th quantum electromagnetic particle is defined as Among them, r1, r2, r3, r4 and r5 are random numbers that are uniformly distributed between [0,1]. is the proportionality coefficient, is the qth dimension of an arbitrarily selected positively polarized particle of the tth generation labeled β, is the qth dimension of an arbitrarily selected negatively polarized particle of the tth generation labeled ζ, The tth generation number is arbitrarily selected as The qth dimension of a neutral particle, is the qth dimension of the kth local optimal measurement position of the tth generation, is the qth dimension of the tth generation global optimal measurement position,

[0076] Then the evolution of the q-dimensional quantum position of the k-th electromagnetic particle of the t+1th generation is defined as in, is a random number that follows a uniform distribution between [0,1], c1 represents the probability of performing a reversal operation on the quantum bit when the quantum rotation angle is 0, and its value is a constant between [0,1 / Q];

[0077] Finally, the quantum position is measured to obtain the corresponding measurement position The measurement equation is in, is a random number between [0, 1] that follows uniform distribution, q = 1, 2, ..., Q. The rest of this implementation is the same as the first specific implementation.

[0078] Specific implementation plan seven: Step 4 includes the following steps:

[0079] The quantum rotation angle and quantum position of the obtained electromagnetic particle are updated again. The quadratic update equation for the q-dimensional quantum rotation angle of the k-th quantum electromagnetic particle is defined as Among them, u1 and u2 are random numbers that obey the standard Gaussian distribution. represents the qth dimension of the average of the K local optimal measurement positions in the tth generation, q = 1, 2, ..., Q, is the qth dimension of the global optimal measurement position of the tth generation; the evolution equation of the qth dimension quantum position of the kth quantum electromagnetic particle of the t+1th generation is defined as in, is a random number uniformly distributed between [0,1]; then the measured position The measurement equation is in, is a random number between [0, 1] that follows uniform distribution, q = 1, 2, ..., Q. The rest of this implementation is the same as the first specific implementation.

[0080] Specific implementation scheme eight: Step 5 includes the following steps:

[0081] The measured position of the kth particle after update Convert to distance vector Substitute the fitness function to obtain the fitness value of the kth particle measurement position; then update the local optimal measurement position of the kth particle to determine whether the updated fitness value satisfies If it is satisfied, then the local optimal measurement position of the kth particle of the t+1th generation is updated to Otherwise, the local optimal measurement position of the t+1th generation remains unchanged from the previous generation. Finally, the global optimal measurement position is updated and the updated local optimal measurement position is The one with the smallest fitness value Denoted as the global optimal measurement position of the t+1th generation quantum electromagnetic particle The rest of this embodiment is the same as the first specific embodiment.

[0082] Specific implementation scheme nine: A distributed array layout structure optimization system based on discrete quantum electromagnetic field optimization mechanism, the system has a program module corresponding to the steps of any of the above implementation schemes one to eight, and executes the steps in the above distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism during operation.

[0083] Specific implementation scheme ten: A computer-readable storage medium, the computer-readable storage medium storing a computer program, the computer program being configured to implement the steps in the distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism described in any one of implementation schemes one to eight when called by a processor.

[0084] Example 1

[0085] The following simulation experiment is used to illustrate the method of the present invention.

[0086] First, the parameters of the distributed array system to be optimized are set, where the total number of array elements in the distributed system is M = 100, the number of array elements in each subarray is G = 10, and the array element spacing in the subarray is The total number of sub-arrays Z = 10, and the overall arrangement of each sub-array is as follows Figure 2 Then the parameters of the discrete quantum electromagnetic field optimization mechanism are set as follows: the number of quantum electromagnets K = 40, the spatial dimension Q = 72, the number of termination iterations T = 300, the proportion of positive polarity particles Neutral particle ratio Percentage of negative polarity examples Selection probability ρ = 0.2, Finally, the total number of auxiliary signal sources in the method is set to 4, and the incident directions are θ1=55.74°, θ2=15.64°, θ3=5.76°, and θ4=-30.5°, respectively. The spatial noise is additive impulse noise, the characteristic index α=1.5, and the generalized signal-to-noise ratio is 3dB. For the direction of arrival estimation method of the signal source, the algorithm spectrum peak search step is set to 0.1°, the number of sampling snapshots L=20, and τ=1.2.

[0087] Based on the above parameter settings, the optimal value is obtained by multiple tests to obtain the final distributed array system layout structure, and the spacing between adjacent sub-arrays is: in,

[0088] In order to further verify the superiority of the obtained subarray layout structure, the obtained layout structure is compared with the conventional evenly spaced distribution array, including the subarray spacing of and Three uniformly arranged structure distributed arrays and uniform linear arrays are jointly simulated and compared, which are referred to as 10 times spacing, 20 times spacing, 50 times spacing and ULA. The direction finding performance of these array structures is tested under harsh conditions such as small snapshots, impact noise and low signal-to-noise ratio. The reference source azimuth information in space, the noise environment and the direction of arrival estimation method all use the same parameter settings as above.

[0089] like Figure 3 As shown in the figure, when the generalized signal-to-noise ratio is 10dB under impulse noise, the influence of the number of sampling snapshots on the direction finding accuracy of different array structures is shown. 200 Monte Carlo tests are performed independently under each number of snapshots. It can be seen that under impulse noise and small snapshots, the array structure of this embodiment has obvious advantages, and the root mean square error of direction finding is much smaller than that of several other common array arrangement structures, and with the increase of the number of snapshots, its direction finding performance continues to improve.

[0090] Figure 4 As shown, when the generalized signal-to-noise ratio is 10dB under impulse noise, the influence of the number of sampling snapshots on the success probability of direction finding estimation of different array structures is shown. It is stipulated that when the error between the estimated value and the target is less than 2.5°, the estimation is judged to be successful. 200 Monte Carlo tests are performed independently under each number of snapshots. It can be seen that under small snapshots, the array structure of this embodiment has higher direction finding accuracy and robustness than other structures, and has obvious advantages. It also shows that the layout structure of this embodiment can adapt to the application conditions of small snapshots, breaking through the limitations of existing layout structures.

[0091] Figure 5 As shown in the figure, when the number of sampling snapshots is 20, the generalized signal-to-noise ratio under impulse noise affects the success probability of direction finding estimation of different array structures. It is stipulated that when the error between the estimated value and the target is less than 2.5°, the estimation is considered successful. 200 Monte Carlo tests were performed independently under each generalized signal-to-noise ratio. It can be seen that under small snapshots and small signal-to-noise ratios, the array structure of this embodiment has higher direction finding performance than other structures, and with the continuous improvement of signal-to-noise conditions, the direction finding performance is greatly improved, which has significant advantages.

[0092] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism, characterized in that: The steps include: Step 1: Set the auxiliary source information and noise information in space to simulate the direction finding performance of the test distributed array in actual applications, and use the calculated source azimuth mean square error as the optimization target equation; Step 2: Initialize the quantum electromagnetic particle information in the quantum electromagnetic field to obtain the local optimal measurement position set and the global optimal measurement position; Step 3: Use an evolutionary strategy based on a quantum rotation gate to update the quantum rotation angle and quantum position of each electromagnetic particle and obtain the measured position; Step 4: Use the evolution strategy 2 based on the quantum rotation gate to update the quantum rotation angle and quantum position of the updated electromagnetic particle again and obtain the measured position; Step 5: Update the local optimal measurement position and the global optimal measurement position of the particle; Step 6: Determine whether the termination iteration number T has been reached. If so, output the global optimal measurement position Q is the maximum dimension of the solution space; otherwise, let algebra t = t + 1, repeat steps 3 to 5, and continue iterating; Step 7: Convert the output global optimal solution into the required array layout structure information.

2. The distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to claim 1 is characterized in that: Step 1 includes the following steps: Step 1-1: Arrange N narrow-band far-field auxiliary signal sources with known positions and wavelengths in space, where The azimuth of the source is Set the noise in the space to impact noise, and set the necessary parameters of the distributed array to be optimized; Step 1-2: For any given distributed array layout structure The distributed array is placed in a space where auxiliary information sources and corresponding noise environments are set up. At this time, the mathematical model of the l-th snapshot sampling data received by the array is B(l)=A(θ)s(l)+n(l), where l=1,2,…,L, L is the maximum number of snapshots, and B(l)=[B1(l),B2(l),…,B M (l)] T is the lth snapshot data vector of M×1 dimension received by the array, the superscript T indicates transposition, s(l) is the N×1 dimensional signal vector, n(l) is the M×1 dimensional impulse noise vector obeying the SαS stable distribution, A(θ)=[a(θ1),a(θ2),…,a(θ N )] is an M×N dimensional array flow matrix, where θ = [θ1,θ2,…,θ N ]The azimuth vector of the source, is the first in the distributed array flow matrix A steering vector, The distributed array as a whole takes the origin as the reference point, and sets the position of the first array element as the origin. is the position of the mth array element; Step 1-3: Calculate the estimated mean square error of the signal source as the optimization target to evaluate the direction finding performance of the distributed array. The target equation to be optimized is: in, is the estimated mean square error of the source, is the potential solution of any given distributed array position structure, N is the number of known sources in space, For the The accurate azimuth of the auxiliary signal source, By obedience The azimuth angle estimated by the ranging distribution array, 3. The distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to claim 2 is characterized in that: The necessary parameters of the distributed array to be optimized are set as described in step 1-1, including: setting the total number of array elements of the distributed system to M, the number of array elements of each subarray to G, using a uniform linear array arrangement, and the array element spacing in the subarray to The total number of subarrays is Z, M = G × Z. The arrangement principle of each subarray in space refers to the one-dimensional arrangement as a whole, and the spacing d between each subarray h The array element spacing is An integer multiple of h=1,…,Z-1; therefore, the spacing vector between sub-arrays is d=[d1,d2,…,d Z-1 ], which is the layout structure information of the distributed array to be optimized, which can be further simplified to obtain 4. The distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to claim 2 is characterized in that: Step 1-2 also includes the need to perform shock removal on the received data, specifically: First construct the fractional low-order matrix The processing method is that the element in row i and column j for In the formula, B i (l) and B j (l) respectively represent the first and second snapshot signal data vectors received by the array. Peace dimension, l=1,2,…,L,1≤i≤M,1≤j≤M,(·)* represents the conjugate operation,τ is the low-order moment number,1≤τ≤α,α is the impulse noise characteristic index; Then, the logarithmic low-order covariance matrix Further processing, its features are decomposed into in, It is a signal subspace spanned by the eigenvectors corresponding to N large eigenvalues. is a diagonal matrix of corresponding large eigenvalues, is the noise subspace spanned by the eigenvectors corresponding to the remaining smaller eigenvalues, Is a diagonal matrix composed of the remaining smaller eigenvalues; get the spatial spectrum equation to be searched Finally, by searching the spectrum peak, we find the angle vectors corresponding to the N maximum values ​​of the constructed space spectrum equation. That is, the estimated source wave direction under the distributed array layout s.

5. The distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to claim 1 is characterized in that: In step 2, the quantum electromagnetic particle information in the quantum electromagnetic field is initialized to obtain a set of local optimal measurement positions and a global optimal measurement position, including the following steps: Step 2-1: First, assume that there are K quantum electromagnetic particles in the electromagnetic field. Each electromagnetic particle has its own quantum position, and the initial value of its quantum position is randomly generated between [0,1]. Then the quantum position of the kth quantum electromagnetic particle of the tth generation is q=1,2,…,Q, and then the corresponding measurement position of the particle is obtained by measurement The measurement equation is in, is a random number that follows a uniform distribution between [0,1]. Q is the maximum dimension of the solution space; Step 2-2: First, the Q-bit binary number Per The form of a group of bits is converted into a Z-1 decimal integer array, which is the potential solution of the array layout structure corresponding to the kth quantum electromagnetic particle of the tth generation. Z is the total number of sub-arrays; finally, the distance vector obtained after transformation Substitute the fitness function N is the number of known sources in space, For the The azimuth of the source, By obedience The azimuth angle estimated by the distance measurement distribution array is used to obtain the fitness value of the k-th quantum electromagnetic particle of the t-th generation; Step 2-3: Record the optimal measurement position of the kth quantum electromagnetic particle after the tth generation optimization search as the local optimal position The measurement position of the entire electromagnetic field with the best fitness value up to the current time is recorded as the global optimal position Set the number of termination iterations to T.

6. The distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to claim 1 is characterized in that: Step 3 includes the following steps: First, set a selection probability ρ, and then generate a random number between [0,1] that follows a uniform distribution If it is less than the selection probability ρ, the q-th quantum rotation angle of the k-th quantum electromagnetic particle of the t+1th generation is defined as in, is a random number that follows a uniform distribution between [0,1]. q=1,2,…,Q, Q is the maximum dimension of the solution space, is the quantum position of the kth quantum electromagnetic particle of the tth generation, If it is greater than or equal to the selection probability ρ, then the q-th quantum rotation angle of the k-th quantum electromagnetic particle is defined as Among them, r1, r2, r3, r4 and r5 are random numbers that are uniformly distributed between [0,1]. is the proportionality coefficient, is the qth dimension of an arbitrarily selected positively polarized particle of the tth generation labeled β, is the qth dimension of an arbitrarily selected negatively polarized particle of the tth generation labeled ζ, The tth generation number is arbitrarily selected as The qth dimension of a neutral particle, is the qth dimension of the kth local optimal measurement position of the tth generation, is the qth dimension of the tth generation global optimal measurement position, q=1,2,…,Q; Then the evolution of the q-dimensional quantum position of the k-th electromagnetic particle of the t+1th generation is defined as in, is a random number that follows a uniform distribution between [0,1], c1 represents the probability of performing a reversal operation on the quantum bit when the quantum rotation angle is 0, and its value is a constant between [0,1 / Q]; Finally, the quantum position is measured to obtain the corresponding measurement position The measurement equation is in, is a random number that follows a uniform distribution between [0,1], q=1,2,…,Q.

7. The distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to claim 1 is characterized in that: Step 4 includes the following steps: The quadratic update equation for the qth-dimensional quantum rotation angle of the kth quantum electromagnetic particle is defined as Among them, u1 and u2 are random numbers that obey the standard Gaussian distribution. represents the qth dimension of the average value of the K local optimal measurement positions in the tth generation, q = 1, 2, ..., Q, Q is the maximum dimension of the solution space, is the qth dimension of the kth local optimal measurement position of the tth generation, is the qth dimension of the global optimal measurement position of the tth generation, is a random number that follows a uniform distribution between [0,1]. is the quantum position of the kth quantum electromagnetic particle of the tth generation, The evolution equation defining the q-dimensional quantum position of the k-th quantum electromagnetic particle of the t+1th generation is: Where c1 represents the probability of performing a reversal operation on the quantum bit when the quantum rotation angle is 0, and its value is a constant between [0,1 / Q], is a random number uniformly distributed between [0,1]; then the measured position The measurement equation is in, is a random number that follows a uniform distribution between [0,1], q=1,2,…,Q.

8. The distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to claim 1 is characterized in that: Step 5 includes the following steps: The measured position of the kth particle after update Convert to distance vector Substitute the fitness function to obtain the fitness value of the kth particle measurement position, where Z is the total number of subarrays; then update the local optimal measurement position of the kth particle to determine whether the updated fitness value satisfies If it is satisfied, then the local optimal measurement position of the kth particle of the t+1th generation is updated to Otherwise, the local optimal measurement position of the t+1th generation remains unchanged from the previous generation. Finally, the global optimal measurement position is updated and the updated local optimal measurement position is The one with the smallest fitness value Denoted as the global optimal measurement position of the t+1th generation quantum electromagnetic particle K is the number of quantum electromagnetic particles.

9. A distributed array layout structure optimization system based on discrete quantum electromagnetic field optimization mechanism, characterized in that: The system has a program module corresponding to the steps of any one of claims 1 to 8, and executes the steps of the distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism when running.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps in the distributed array layout structure optimization method based on discrete quantum electromagnetic field optimization mechanism according to any one of claims 1 to 8 when called by a processor.

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