A quantum observation based sparse array design method

Abstract

This invention discloses a sparse array design method based on quantum observation, belonging to the field of array antenna synthesis design technology. The method includes: pre-introducing quantum observation into the design of a sparse array antenna under multiple constraints, setting the state of the array elements to require observation for confirmation; at this point, the probability of each element being observed and activated is different; during the optimization iteration, the individual elements in the optimization iteration algorithm represent the probability distribution of all array arrangement schemes, using the probability of an element being observed as the optimization variable in the optimization iteration algorithm, and iterating to optimize the probability of the array arrangement schemes, obtaining the array arrangement with the highest probability as the optimal arrangement. This invention effectively improves optimization performance.

Description

Technical Field

[0001] This invention relates to the field of array antenna integrated design technology, and more specifically, to a sparse array design method based on quantum observation. Background Technology

[0002] Compared to traditional uniform arrays with equal spacing, sparse arrays can reduce hardware costs by decreasing the number of elements. Furthermore, the extremely high degree of freedom in the arrangement of antenna elements allows for better radiation characteristics, such as PSLL (Power Scattered Linear Envelope). Therefore, sparse arrays are widely used in phased arrays, radar, and wireless communications. However, designing sparse arrays under multiple constraints (fixed array aperture size, fixed number of elements, minimum element spacing) requires optimizing the position of all elements, which is a complex multidimensional nonlinear optimization problem.

[0003] To obtain radiation patterns and performance that meet specific requirements, such as low sidelobes, design optimization of sparse arrays is essential. Common design methods include numerical analysis and intelligent optimization algorithms. Intelligent optimization algorithms, due to their efficiency and wide applicability, have become one of the commonly used optimization design methods. In traditional iterative optimization algorithms, each individual in the swarm represents a definite array arrangement, and optimization iteration involves updating and comparing these arrangements. This is feasible for small-scale array design optimization. However, as the array size increases and the number of variables grows, the array arrangement schemes increase exponentially. To ensure optimization effectiveness, the algorithm swarm size and the number of iterations also increase accordingly, leading to the curse of dimensionality. This technical problem urgently needs to be solved by researchers in this field. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a sparse array design method based on quantum observation, which effectively improves the optimization performance.

[0005] The objective of this invention is achieved through the following solution: A sparse array design method based on quantum observation includes the following steps: In the design of sparse array antennas under multiple constraints, quantum observation is introduced in advance, and the state of the units that make up the array is set to require observation to be confirmed; at this time, the probability of each unit being activated by observation is different. When entering the optimization iteration, the individual in the optimization iteration algorithm represents the probability distribution of all array arrangement schemes. The probability of the cell being observed is used as the optimization variable in the optimization iteration algorithm. The probability of the array arrangement scheme is optimized and iterated, and the array arrangement with the highest probability is the optimal arrangement.

[0006] Furthermore, the multiple constraints include a fixed array aperture size, a fixed number of elements, and a minimum element spacing.

[0007] Furthermore, the state of the unit includes an enabled or disabled state.

[0008] Furthermore, the sparse array antenna design under multiple constraints incorporates quantum observation in advance, setting the state of the array's units to require observation for confirmation. This specifically includes the following sub-steps: Let the unit state function be... Determining this requires observation, matrix Used to represent the overall observation of the array, where Let m represent the probability that a cell in row m and column n is observed and turned on. Then, through the matrix... The process of determining the array state is as follows: Step S1: Determine the size of the uniform array based on the array size L×H. Design the following constraints: ; In the formula, N represents the total number of minimum array elements. Indicates the minimum spacing between elements. Represents positive integers; Step S2: Initialize the probability matrix According to the matrix The data in the data is arranged in sequential units; Step S3: Determine the state of the unit based on the sorting, and express the state function based on the following formula. : ; Where r is a random number uniformly distributed between 0 and 1, and is a P×Q matrix.

[0009] Furthermore, in step S2, the sequentially arranged units are specifically arranged in descending order, with higher probability units ranking higher.

[0010] Furthermore, the step of using the probability of a cell being observed as an optimization variable in the optimization iterative algorithm to optimize the probability of the array arrangement scheme's occurrence, and obtaining the array arrangement with the highest probability, is the optimal arrangement, specifically includes the following sub-steps: Step S4: After confirming the status of all cells, count the number of opened elements. ;if Then the last An element changes from an open state to a closed state; if Then, reorder all remaining elements and perform a state check until... Multiple selections and use the one with the best performance An array, or minimum PSLL, is used to represent an individual; in this case, the fitness function is expressed as: ; in, express Number of selections; Step S5: After determining all individual algorithms, i.e., after determining all state functions S, calculate the individual performance, i.e., the sidelobes, according to the following formula: ; Where AF represents the array pattern, Fit( ) represents the sum of the sidelobes of the orthogonal planes of the array pattern under the current sparse scheme; Step S6: Use a heuristic iterative algorithm to process the probability matrix of the individual. Update; Step S7: Repeat steps S2 to S6 until the maximum number of iterations is reached or the requirements are met.

[0011] Furthermore, in step S6, the heuristic iterative algorithm includes the differential evolution algorithm.

[0012] Furthermore, the array parameters under the multiple constraints include: aperture size of 2L×2H=9.5λ×4.5λ, where λ is the wavelength, minimum element spacing d0 is set to 0.5λ, and the number of array elements is 4N.

[0013] Furthermore, the possible values ​​of N include 27.

[0014] The beneficial effects of this invention include: This invention incorporates the concept of quantum observation, overcoming the limitation of individual algorithms in antenna array design that can only represent a single scheme. By utilizing the uncertainty of quantum observation results, the algorithm can probabilistically represent all array configurations, effectively improving optimization performance. Results show that the proposed design method has a good optimization effect on suppressing sidelobe levels when solving sparse array design problems. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the overall steps of the method of the present invention; Figure 2 The probability distribution of the optimal algorithm individual at different iteration numbers; Figure 3 This is a comparison (quarter array) of the optimal distribution obtained by the method of this invention embodiment and the method in related literature; Figure 4 This is the orientation pattern (orthogonal plane) of the sparse array of the method in the embodiment of the present invention. Detailed Implementation

[0017] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0018] Given the current situation, the inventors of this invention, after creative thinking, believe that this invention aims to solve the problem of excessively large solution space and low optimization performance in sparse array design under multiple constraints (fixed array face size, fixed number of cells, minimum cell spacing). The invention introduces the concept of quantum observation, where the state (enabled / disabled) of the cells forming the array can only be confirmed through observation. This means that each individual cell no longer represents a single array arrangement, but theoretically can represent all array arrangements, and the probability of each cell being observed as enabled is different, meaning different arrays have different probabilities of occurrence. In this case, the individual cell represents the probability distribution of all array arrangement schemes. Under this strategy, the optimization variable is no longer the cell state, but the probability of the cell being observed. The optimization iteration is the optimization adjustment of the probability of occurrence of all array arrangement schemes. Obviously, with optimization iteration, the array arrangement with the highest probability is the optimal arrangement.

[0019] In a more specific implementation, embodiments of the present invention provide a sparse array design method based on quantum observation, such as... Figure 1 As shown, the specific implementation process is as follows: Let the unit state function be... Determining this requires observation, matrix Used to represent the overall observation of the array, where Let m represent the probability that a cell in row m and column n is observed and turned on. Then, through the matrix... The process of determining the array state is as follows: Step S1: Determine the size of the uniform array, i.e., P and Q, based on the array size (L×H). The confirmation principles are as follows: ; In the formula, N represents, Indicates the minimum spacing between elements; Step S2: Initialize the probability matrix According to the matrix The data in the data is arranged in descending order: the higher the probability, the higher the ranking. Step S3: Determine the state of the cell based on the sorting. The state function is based on the following criteria. It can be represented as: ; Where r is a random number uniformly distributed between 0 and 1, and is a P×Q matrix; Step S4: After confirming the status of all cells, count the number of opened elements. .if Then the last An element changes from an open state to a closed state; if Then, reorder all remaining elements and perform a state check until... .

[0020] However, since individual algorithms can probabilistically represent any array, i.e. It's not fixed; multiple selections are made to accelerate convergence while ensuring the algorithm's search accuracy. and use the one with the best performance An array, or minimum PSLL, is used to represent an individual. Therefore, the fitness function can be expressed as: ; in, express Number of selections.

[0021] Step S5: After determining all individual algorithms, i.e., after determining all state functions S, calculate the individual performance, i.e., the sidelobes. The calculation formula is as follows: ; Where AF represents the array pattern and Fit(S) represents the sum of the sidelobes of the orthogonal plane of the array pattern under this sparse scheme.

[0022] Step S6: Use a heuristic iterative algorithm to process the probability matrix of the individual. Updates can be made, such as using differential evolution algorithms.

[0023] Step S7: Repeat steps S2 to S6 until the maximum number of iterations is reached or the requirements are met.

[0024] In summary, the embodiments of this invention propose a design method that incorporates quantum observation probability for the sparse array design problem under multiple constraints. By utilizing the uncertainty of quantum observation results, the representation ability of individual algorithms is effectively improved. This transforms the traditional algorithm from representing only a single scheme to probabilistically representing all schemes. Under this technical approach, the sparse array is optimized and designed, effectively improving array performance.

[0025] In other embodiments, based on the above embodiments, a sparse array design method based on quantum observation is provided, which optimizes an example of a non-equidistant symmetric rectangular array. The array parameters are: aperture size 2L×2H=9.5λ×4.5λ (λ is the wavelength), minimum element spacing d0 is set to 0.5λ, and the number of array elements is 4N (where N is 27 in this embodiment; note that in this embodiment, the array is divided into four parts according to axisymmetry, and only the layout of one part needs to be optimized, while the remaining parts are symmetrically distributed). An algorithm is used to perform sparsification optimization on the entire array, with iterations T=300 and a population size N. p =100. The detailed process includes the following steps: Step 1: Set P according to the array size (2L×2H=9.5λ×4.5λ) and minimum spacing constraint (d0=0.5λ). max Q max The sizes are 10 and 20 respectively. According to the principle of axisymmetry, the size of the quarter array that needs to be optimized is 5×10 (P×Q). Due to the constraint of the array aperture, the array element in the 5th row and 10th column (i.e., coordinates (4.75λ, 2.25λ)) is always in the open state.

[0026] Step 2: Initialize the probability matrix According to the matrix The data in the data is arranged in descending order: the higher the probability, the higher the ranking. Step 3: Determine the state of the unit based on the sorting. The state function is based on the following criteria. It can be represented as:

[0027] Where r is a random number uniformly distributed between 0 and 1, and is a P×Q matrix.

[0028] Step 4: After confirming the status of all cells, count the number of opened elements. .if Then the last An element changes from an open state to a closed state; if Then, reorder all remaining elements and perform a state check until... .

[0029] Step 5: After determining all individual algorithms, i.e., after determining all state functions S, calculate the individual performance, i.e., the sidelobe, using the following formula.

[0030]

[0031] Where AF represents the array pattern and Fit(S) represents the sum of the sidelobes of the orthogonal plane of the array pattern under this sparse scheme.

[0032] Step 6: Use the differential evolution algorithm to process the probability matrix of the individual. Update.

[0033] Step 7: Repeat steps 2 to 6 until the maximum number of iterations is reached or the requirement is met. The optimal distribution S obtained in this example is as follows.

[0034] Figure 2 The probability matrix distribution of the optimal individual is shown at different iteration numbers. Figure 3 The paper presents a comparison between the algorithm of this invention and the optimal distribution obtained from related literature (quarter array). Figure 4 The radiation pattern (orthogonal plane) of the sparse array is shown.

[0035] It should be noted that, within the scope of protection defined in the claims of this invention, the following embodiments can be combined and / or extended or replaced in any logical manner from the above specific embodiments, such as the disclosed technical principles, disclosed technical features or implicitly disclosed technical features.

[0036] Example 1 A sparse array design method based on quantum observation includes the following steps: In the design of sparse array antennas under multiple constraints, quantum observation is introduced in advance, and the state of the units that make up the array is set to require observation to be confirmed; at this time, the probability of each unit being activated by observation is different. When entering the optimization iteration, the individual in the optimization iteration algorithm represents the probability distribution of all array arrangement schemes. The probability of the cell being observed is used as the optimization variable in the optimization iteration algorithm. The probability of the array arrangement scheme is optimized and iterated, and the array arrangement with the highest probability is the optimal arrangement.

[0037] Example 2 Based on Example 1, the multiple constraints include fixed array aperture size, fixed number of elements, and minimum element spacing.

[0038] Example 3 Based on Embodiment 1, the state of the unit includes an enabled or disabled state.

[0039] Example 4 Based on Example 3, the pre-introduction of quantum observation in the sparse array antenna design under multiple constraints, whereby the state of the units constituting the array is set to require observation for confirmation, specifically includes the following sub-steps: Let the unit state function be... Determining this requires observation, matrix Used to represent the overall observation of the array, where Let m represent the probability that a cell in row m and column n is observed and turned on. Then, through the matrix... The process of determining the array state is as follows: Step S1: Determine the size of the uniform array based on the array size L×H. Design the following constraints: ; In the formula, N represents the total number of minimum array elements. Indicates the minimum spacing between elements. Represents positive integers; Step S2: Initialize the probability matrix According to the matrix The data in the data is arranged in sequential units; Step S3: Determine the state of the unit based on the sorting, and express the state function based on the following formula. : ; Where r is a random number uniformly distributed between 0 and 1, and is a P×Q matrix.

[0040] Example 5 Based on Example 4, in step S2, the sequentially arranged units are specifically arranged in descending order, with higher probability units ranking higher.

[0041] Example 6 Based on Example 4, the step of using the probability of a cell being observed as an optimization variable in the optimization iteration algorithm to optimize the probability of the array arrangement scheme to obtain the array arrangement with the highest probability is the optimal arrangement. This specifically includes the following sub-steps: Step S4: After confirming the status of all cells, count the number of opened elements. ;if Then the last An element changes from an open state to a closed state; if Then, reorder all remaining elements and perform a state check until... Multiple selections and use the one with the best performance An array, or minimum PSLL, is used to represent an individual; in this case, the fitness function is expressed as: ; in, express Number of selections; Step S5: After determining all individual algorithms, i.e., after determining all state functions S, calculate the individual performance, i.e., the sidelobes, according to the following formula: ; Where AF represents the array pattern, Fit( ) represents the sum of the sidelobes of the orthogonal planes of the array pattern under the current sparse scheme; Step S6: Use a heuristic iterative algorithm to process the probability matrix of the individual. Update; Step S7: Repeat steps S2 to S6 until the maximum number of iterations is reached or the requirements are met.

[0042] Example 7 Based on Example 6, in step S6, the heuristic iterative algorithm includes the differential evolution algorithm.

[0043] Example 8 Based on Example 4, the array parameters under the multi-constraint conditions include: aperture size of 2L×2H=9.5λ×4.5λ, where λ is the wavelength, minimum element spacing d0 is set to 0.5λ, and the number of array elements is 4N.

[0044] Example 9 Based on Example 8, the value of N includes 27.

[0045] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0046] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0047] In another aspect, embodiments of the present invention also provide a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

Claims

1. A sparse array design method based on quantum observation, characterized in that, Includes the following steps: In the design of sparse array antennas under multiple constraints, quantum observation is introduced in advance, and the state of the units that make up the array is set to require observation to be confirmed; at this time, the probability of each unit being activated by observation is different. When entering the optimization iteration, the individual in the optimization iteration algorithm represents the probability distribution of all array arrangement schemes. The probability of the cell being observed is used as the optimization variable in the optimization iteration algorithm. The probability of the array arrangement scheme is optimized and iterated, and the array arrangement with the highest probability is the optimal arrangement.

2. The sparse array design method based on quantum observation according to claim 1, characterized in that, The multiple constraints include fixed array aperture size, fixed number of elements, and minimum element spacing.

3. The sparse array design method based on quantum observation according to claim 1, characterized in that, The state of the unit includes enabled or disabled states.

4. The sparse array design method based on quantum observation according to claim 3, characterized in that, The sparse array antenna design under multiple constraints incorporates quantum observation in advance, setting the state of the array's units to require observation for confirmation. This includes the following sub-steps: Let the unit state function be... Determining this requires observation, matrix Used to represent the overall observation of the array, where Let m represent the probability that a cell in row m and column n is observed and turned on. Then, through the matrix... The process of determining the array state is as follows: Step S1: Determine the size of the uniform array based on the array size L×H. Design the following constraints: ； In the formula, N represents the total number of minimum array elements. Indicates the minimum spacing between elements. Represents positive integers; Step S2: Initialize the probability matrix According to the matrix The data in the data is arranged in sequential units; Step S3: Determine the state of the unit based on the sorting, and express the state function based on the following formula. : ； Where r is a random number uniformly distributed between 0 and 1, and is a P×Q matrix.

5. The sparse array design method based on quantum observation according to claim 4, characterized in that, In step S2, the sequentially arranged units are specifically arranged in descending order, with higher probability units ranking higher.

6. The sparse array design method based on quantum observation according to claim 4, characterized in that, The step of using the probability of a cell being observed as an optimization variable in the optimization iterative algorithm to optimize the probability of the array arrangement schemes and obtain the array arrangement with the highest probability is the optimal arrangement. This specifically includes the following sub-steps: Step S4: After confirming the status of all cells, count the number of opened elements. ;if Then the last An element changes from an open state to a closed state; if Then, reorder all remaining elements and perform a state check until... Multiple selections and use the one with the best performance An array, or minimum PSLL, is used to represent an individual; in this case, the fitness function is expressed as: ； in, express Number of selections; Step S5: After determining all individual algorithms, i.e., after determining all state functions S, calculate the individual performance, i.e., the sidelobes, according to the following formula: ； Where AF represents the array pattern, Fit( ) represents the sum of the sidelobes of the orthogonal planes of the array pattern under the current sparse scheme; Step S6: Use a heuristic iterative algorithm to process the probability matrix of the individual. Update; Step S7: Repeat steps S2 to S6 until the maximum number of iterations is reached or the requirements are met.

7. The sparse array design method based on quantum observation according to claim 6, characterized in that, In step S6, the heuristic iterative algorithm includes the differential evolution algorithm.

8. The sparse array design method based on quantum observation according to claim 4, characterized in that, The array parameters under the multiple constraints include: aperture size of 2L×2H=9.5λ×4.5λ, where λ is the wavelength, minimum element spacing d0 is set to 0.5λ, and the number of array elements is 4N.

9. The sparse array design method based on quantum observation according to claim 8, characterized in that, The possible values ​​of N include 27.

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