Low-peak sidelobe level sparse array optimization method for constraint direction angle range

Abstract

The invention relates to the technical field of radar signal optimization, and discloses a low-peak sidelobe level sparse array optimization method in a constraint direction angle range, which comprises the following steps: traversing an initial array, corroding array elements except two ends in the initial array one by one to obtain a plurality of intermediate arrays, and setting the intermediate arrays as original intermediate arrays; judging whether the number of array elements of each original middle array is greater than a set threshold value, if so, selecting a plurality of arrays with minimum peak side lobes within a constraint direction angle range from the plurality of original middle arrays as new middle arrays, and then traversing the new intermediate array and corroding the array elements except for the two ends in the new intermediate array one by one to obtain a plurality of arrays, enabling the obtained arrays to be the original intermediate array, and then returning to the step, otherwise, selecting the new intermediate array with the minimum constraint direction angle peak side lobe as the optimal sparse array. According to the method, the optimal sparse array distribution with low PSL in a certain direction angle range can be quickly found.

Description

Technical Field

[0001] The invention relates to the technical field of radar signal optimization, and in particular to a low peak sidelobe level sparse array optimization method with a constrained directional angle range. Background Art

[0002] Within the array aperture range, sparse arrays can achieve the effect of scanning narrow beams and high resolution with fewer array elements, thereby reducing production costs and have been successfully applied in radar systems. In some practical application scenarios, if the sidelobes in a certain angular range are of concern, the sidelobes in the angular range of concern can be sacrificed to make the sidelobes in the angular range of concern better. The peak sidelobe level in the constrained angular range of the array antenna is an important indicator for evaluating its performance.

[0003] In addition, the sidelobe of the unoptimized sparse array is relatively high, and the position distribution of the sparse array elements affects the sidelobe level performance. Therefore, research on how to reasonably optimize the array element position to make the low peak sidelobe low within the constrained angular range has very important theoretical and practical significance in the sparse array synthesis problem.

[0004] The current optimization method for sparse arrays is the exhaustive method, whose computational complexity increases exponentially with the number of array elements. Therefore, it is only suitable for the optimization design of small sparse arrays.

[0005] Traditional algorithms such as genetic algorithms and particle swarm algorithms are random and require a lot of calculations to get the optimization results; and the results obtained each time are different. In order to get more optimized results, a large number of initial random seeds need to be generated. In addition, various parameters such as the number of initial random seeds need to be set. The size of the parameter setting will also affect the optimization results, so the algorithm is difficult to control.

[0006] The current research mainly focuses on all sidelobe levels, and there is no research on the optimization of the peak sidelobe level within the constrained angular range. Summary of the invention

[0007] The invention provides a sparse array optimization method for low peak sidelobe level in a constrained directional angle range, which optimizes the peak sidelobe level based on the constrained directional angle range.

[0008] The present invention is achieved through the following technical solutions:

[0009] A method for optimizing a sparse array with low peak sidelobe level in a constrained directional angle range, comprising:

[0010] S1, traversing the initial array and corroding each array element except the two ends of the initial array one by one to obtain a number of intermediate arrays, and setting the intermediate arrays as the original intermediate arrays;

[0011] S2. Determine whether the number of array elements of each of the original intermediate arrays is greater than a set threshold value. If so, select several arrays with the smallest peak side lobes within the constrained directional angle range from the several original intermediate arrays as new intermediate arrays, the number of the new intermediate arrays is less than the number of the original intermediate arrays, and then traverse the new intermediate arrays respectively and corrode each array element except the two ends of the new intermediate array one by one to obtain several arrays, and make the obtained arrays the original intermediate arrays, and then return to this step. Otherwise, select the new intermediate array with the smallest peak side lobe of the constrained directional angle as the optimal sparse array.

[0012] As an optimization, the initial array is a uniform regular array.

[0013] As an optimization, during the same number of traversals, the number of array elements in each of the new intermediate arrays is the same, and the number of array elements in each of the original intermediate arrays is the same.

[0014] As an optimization, each array element in the initial array is excited with equal amplitude.

[0015] As an optimization, the aperture of the optimal sparse array is the same as the aperture of the initial array.

[0016] As an optimization, after corroding each array element except the two ends of the initial array one by one to obtain a number of original intermediate arrays, the peak side lobes of the several original intermediate arrays within the constrained directional angle range are calculated respectively.

[0017] As an optimization, S2 can also be:

[0018] Select several arrays with the smallest peak side lobes within the constrained directional angle range from the several original intermediate arrays as new intermediate arrays, traverse each of the new intermediate arrays respectively, and corrode each array element except the two ends of the new intermediate array one by one to obtain several first arrays, and judge whether the number of array elements of each first array is greater than a set threshold. If so, make the first array the original intermediate array, and then return to this step; otherwise, select the first array with the smallest peak side lobe of the constrained directional angle as the optimal sparse array.

[0019] As an optimization, each of the original intermediate arrays or the new intermediate arrays erodes its own array elements one by one in a synchronous manner.

[0020] As an optimization, the set threshold is smaller than the number of array elements in the initial array.

[0021] The present invention also discloses a low peak sidelobe level sparse array optimization system with a constrained directional angle range, comprising:

[0022] An initial array erosion module, used for traversing the initial array and eroding each array element except the two ends of the initial array one by one to obtain a plurality of intermediate arrays, and making the intermediate arrays the original intermediate arrays;

[0023] A judgment module is used to judge whether the number of array elements of each of the original intermediate arrays is greater than a set threshold value. If so, a number of arrays with the smallest peak side lobes within the constrained directional angle range are selected from the several original intermediate arrays as new intermediate arrays, and the number of the new intermediate arrays is less than the number of the original intermediate arrays. Then, the new intermediate arrays are traversed respectively and each array element except the two ends of the new intermediate array is corroded one by one to obtain a number of arrays, and the obtained arrays are made the original intermediate arrays, and then the execution command of the judgment module is repeated. Otherwise, the new intermediate array with the smallest peak side lobe of the constrained directional angle is selected as the optimal sparse array.

[0024] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0025] The present invention provides a new low sidelobe sparse array optimization method with a constrained angular range. The technology has a small amount of calculation and can quickly find the optimal sparse array distribution with low PSL within a certain angular range. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0027] Figure 1 A flowchart of a method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to the present invention;

[0028] Figure 2 To execute the flowchart in one of the embodiments;

[0029] Figure 3 This is an optimal sparse array pattern specifically exemplified in the embodiment. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0031] It should be noted that the optimization of the present invention has certain prerequisites, that is, the implementation of the present invention is to optimize the distribution position of the array elements under the premise of equal amplitude excitation of each array element, constraining the number of sparse array elements, array element aperture, array element spacing, constrained angular range and other constraints, and its main goal is to reduce the peak sidelobe level (PSL) in a certain angular range as much as possible.

[0032] Assume that the grid spacing is d, then the aperture is L = (M - 1)d, where M is the number of array elements in a regular uniform array. The target is to design a sparse array with N (N < M) array elements, and the aperture of the sparse linear array is also L = (M - 1)d. Optimize the positions d of the array elements of the sparse linear array n , so that the peak sidelobe level is the lowest within the range of direction angles θ ∈ [-μ, μ]. Optimize the sparse array

[0033] Next, the optimization process of the present invention will be specifically introduced

[0034] Embodiment 1 of the present invention provides a method for optimizing a sparse array with a low peak sidelobe level by constraining the range of direction angles, including:

[0035] S1. Traverse the initial array and erode each array element in the initial array except the two ends one by one to obtain a number of intermediate arrays, and let this intermediate array be the original intermediate array

[0036] That is, through the foregoing introduction, in some embodiments, the initial array is a uniform regular array. It should be noted that the initial array of the present invention is composed of multiple array elements arranged in a straight line. Therefore, the two ends refer to the array elements at both ends of the straight line. Erosion here means reducing one array element. For example, if the array elements 1, 2, 3, 4, 5, 6, 7, 8 form the initial array 12345678, then traversing the initial array and eroding each array element in the initial array except the two ends one by one to obtain a number of intermediate arrays, the specific process is as follows

[0037] Erode 2, then the intermediate array obtained is: 1345678

[0038] Erode 3, then the intermediate array obtained is: 1245678

[0039] Erode 4, then the intermediate array obtained is: 1235678

[0040] Erode 5, then the intermediate array obtained is: 1234678

[0041] Erode 6, then the intermediate array obtained is: 1234578

[0042] Erode 7, then the intermediate array obtained is: 1234568

[0043] Then, the several original intermediate arrays are 1345678, 1245678, 1235678, 1234678, 1234578, 1234568 respectively

[0044] In the present invention, the reason for not being able to erode the two ends of the initial array is that the aperture size cannot be changed. Eroding the two ends will change the aperture size

[0045] S2. Determine whether the number of array elements of each of the original intermediate arrays is greater than a set threshold value. If so, select several arrays with the smallest peak side lobes within the constrained directional angle range from the several original intermediate arrays as new intermediate arrays, the number of the new intermediate arrays is less than the number of the original intermediate arrays, and then traverse the new intermediate arrays respectively and corrode each array element except the two ends of the new intermediate array one by one to obtain several arrays, and make the obtained arrays the original intermediate arrays, and then return to this step. Otherwise, select the new intermediate array with the smallest peak side lobe of the constrained directional angle as the optimal sparse array.

[0046] Therefore, in some embodiments, after the array elements except the two ends of the initial array are corroded one by one to obtain a number of original intermediate arrays, the peak side lobes of the several original intermediate arrays within the constrained directional angle range are calculated respectively.

[0047] At the same time, each array element except the two ends in the new intermediate array is corroded one by one to obtain a plurality of arrays, and the peak side lobes of these newly obtained arrays within the constrained directional angle range are also calculated respectively.

[0048] Of course, the threshold is usually set to be not less than the number of elements in the initial array by one, so it will need to go through at least two corrosions first. Then, at this time, the step of S2 should be:

[0049] Select several arrays with the smallest peak side lobes within the constrained directional angle range from the several original intermediate arrays as new intermediate arrays, traverse each of the new intermediate arrays respectively, and corrode each array element except the two ends of the new intermediate array one by one to obtain several first arrays, and judge whether the number of array elements of each first array is greater than a set threshold. If so, make the first array the original intermediate array, and then return to this step; otherwise, select the first array with the smallest peak side lobe of the constrained directional angle as the optimal sparse array.

[0050] For example, for example, Figure 2 As shown, that is:

[0051] 1. Set the initial array to a uniform regular array of M elements.

[0052] 2. Corrode an array element. The position of the corroded array element traverses the array except the array elements at both ends, thus obtaining (M-2) arrays (the original middle array).

[0053] 3. For the M-2 original intermediate arrays, calculate the peak side lobes within the constrained directional angle range respectively.

[0054] 4. Select P arrays (called new intermediate arrays) with the smallest peak side lobes within the constrained angular range.

[0055] The number of P is set according to the time requirement of the operation. If the time requirement is fast, P is set to a small value, and the optimization effect will be worse. If the time requirement is not urgent, P is set to a large value, and the optimization effect is good, but the amount of calculation will increase. Therefore, P is set at a compromise to ensure a certain performance and efficiency.

[0056] 5. For the P new intermediate arrays, assuming that the number of array elements of the P new intermediate arrays is Q, erode one array element respectively, and traverse the eroded array elements to obtain a total of (Q-2)*P arrays (called the first arrays), and then calculate the peak sidelobes of each first array within the constrained directional angle range.

[0057] 6. Determine whether the number of array elements of the current array (first array) is greater than N. If so, return to step 4 (regard the first array as the new original intermediate array and then return to step 4). Otherwise, select the array (first array) with the smallest peak sidelobe within the constrained directional angle range as the final result of the optimization.

[0058] N is set to the number of sparse array elements. The smaller the value of N, the lower the cost. Therefore, N can be selected according to actual needs.

[0059] It can be known from the above process that in the same traversal number process, the number of array elements in each of the new intermediate arrays is the same, and the number of array elements in each of the original intermediate arrays is the same.

[0060] In some embodiments, each of the original intermediate arrays or the new intermediate arrays erodes its respective array elements one by one synchronously, which can greatly reduce the calculation time.

[0061] In the simulation, the regular uniform array element M = 64 is used as an example. The above method is used to solve the problem under different sparse array element numbers N, and its peak sidelobe level performance is analyzed. When N = 46, the optimal sparse array has a PSL of about -21.1dB within the constrained angular range [-50°, 50°]. The results are as follows Figure 3 shown.

[0062] Figure 3 Optimal sparse array pattern with N=46.

[0063] In some embodiments, a low peak sidelobe level sparse array optimization system with a constrained directional angle range is also disclosed, including:

[0064] An initial array erosion module, used for traversing the initial array and eroding each array element except the two ends of the initial array one by one to obtain a plurality of intermediate arrays, and making the intermediate arrays the original intermediate arrays;

[0065] A judgment module is used to judge whether the number of array elements of each of the original intermediate arrays is greater than a set threshold value. If so, a number of arrays with the smallest peak side lobes within the constrained directional angle range are selected from the several original intermediate arrays as new intermediate arrays, and the number of the new intermediate arrays is less than the number of the original intermediate arrays. Then, the new intermediate arrays are traversed respectively and each array element except the two ends of the new intermediate array is corroded one by one to obtain a number of arrays, and the obtained array is set as the initial original intermediate array, and then the execution command of the judgment module is repeated. Otherwise, the new intermediate array with the smallest peak side lobe of the constrained directional angle is selected as the optimal sparse array.

[0066] The present invention provides a method for optimizing a low peak sidelobe level sparse array with a constrained angular range, which uses an element corrosion method to obtain a sparse array of N elements, without random factors, and gradually approaches the optimal solution. The array synthesis of its deformation method is also included in the present invention. The method can constrain the PSL of a certain range of angular directions, and its deformation method is also included in the present invention.

[0067] The method purposefully and gradually erodes the regular array elements, and finally obtains a sparse array with low side lobes within the constrained directional angle range. The method proposed by the present invention is purposeful and has no random links, so the optimization target can be quickly achieved with less calculation. In addition, the method proposed by the present invention can be processed in parallel, which greatly saves calculation time and can be applied to the sparse optimization of large arrays.

[0068] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0069] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] It should also be noted that, in this specification, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0071] At the same time, the devices used in this specification are all existing devices, and those skilled in the art can obtain them through purchase, reference, etc.

[0072] In summary, although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the scope of the attached patent application.

[0073] Those skilled in the art should understand that within the scope of the claims and their equivalents, various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors.

[0074] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0076] In summary, although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. A person with ordinary knowledge in the technical field to which the present invention belongs can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the scope of the attached patent application.

[0077] Those skilled in the art should understand that within the scope of the claims and their equivalents, various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors.

[0078] At the same time, the materials and processes used in this specification are all existing and those skilled in the art can obtain them through purchase, reference, etc.

Claims

1. A method for optimizing a sparse array with low peak sidelobe level within a constrained angular range, characterized in that: include: S1, traversing the initial array and corroding each array element except the two ends of the initial array one by one to obtain a number of intermediate arrays, and setting the intermediate arrays as the original intermediate arrays; S2. Determine whether the number of array elements of each of the original intermediate arrays is greater than a set threshold value. If so, select several arrays with the smallest peak side lobes within the constrained directional angle range from the several original intermediate arrays as new intermediate arrays, the number of the new intermediate arrays is less than the number of the original intermediate arrays, and then traverse the new intermediate arrays respectively and corrode each array element except the two ends of the new intermediate array one by one to obtain several arrays, and make the obtained arrays the original intermediate arrays, and then return to this step. Otherwise, select the new intermediate array with the smallest peak side lobe of the constrained directional angle as the optimal sparse array.

2. A method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 1, characterized in that: The initial array is a uniform regular array.

3. The method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 1, characterized in that: During the same number of traversals, the number of array elements in each of the new intermediate arrays is the same, and the number of array elements in each of the original intermediate arrays is the same.

4. The method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 1, characterized in that: Each of the array elements in the initial array is excited with equal amplitude.

5. The method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 1, characterized in that: The aperture of the optimal sparse array is the same as the aperture of the initial array.

6. The method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 1, characterized in that: After corroding each array element except the two ends of the initial array one by one to obtain a number of original intermediate arrays, the peak side lobes of the number of original intermediate arrays within the constrained directional angle range are calculated respectively.

7. The method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 1, characterized in that: S2 can also be: Select several arrays with the smallest peak side lobes within the constrained directional angle range from the several original intermediate arrays as new intermediate arrays, traverse each of the new intermediate arrays respectively, and corrode each array element except the two ends of the new intermediate array one by one to obtain several first arrays, and judge whether the number of array elements of each first array is greater than a set threshold. If so, make the first array the original intermediate array, and then return to this step; otherwise, select the first array with the smallest peak side lobe of the constrained directional angle as the optimal sparse array.

8. The method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 7, characterized in that: Each of the original intermediate arrays or the new intermediate arrays is performed synchronously when corroding the respective array elements one by one.

9. The method for optimizing a sparse array with low peak sidelobe level within a constrained directional angle range according to claim 1, characterized in that: The set threshold is smaller than the number of array elements in the initial array.

10. A low peak sidelobe level sparse array optimization system with constrained directional angle range, characterized in that: include: An initial array erosion module, used for traversing the initial array and eroding each array element in the initial array except the two ends one by one to obtain a plurality of intermediate arrays, and making the intermediate arrays the original intermediate arrays; A judgment module is used to judge whether the number of array elements of each of the original intermediate arrays is greater than a set threshold value. If so, a number of arrays with the smallest peak side lobes within the constrained directional angle range are selected from the several original intermediate arrays as new intermediate arrays, and the number of the new intermediate arrays is less than the number of the original intermediate arrays. Then, the new intermediate arrays are traversed respectively and each array element except the two ends of the new intermediate array is corroded one by one to obtain a number of arrays, and the obtained arrays are made the original intermediate arrays, and then the execution command of the judgment module is repeated. Otherwise, the new intermediate array with the smallest peak side lobe of the constrained directional angle is selected as the optimal sparse array.

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