Discretized region scanning subarray-level sparse optimization method and system

Abstract

The invention discloses a discretized region scanning subarray-level sparse optimization method and system, and belongs to the technical field of array antenna design, and the method comprises the following steps: S1, discretizing an initial population; s2, carrying out region scanning processing; and s3, optimization processing. When the method and the system are applied to sub-array-level sparseoptimization of a large-scale circular aperture antenna, under the condition that the sub-array spacing meets the spacing requirement, the optimization time is greatly reduced, the sidelobe level isreduced, a new way is provided for rapidly solving effective position arrangement of array elements under the specific array element spacing limitation condition, the diversity of a solution set in the optimization process is improved, the probability of finding the optimal solution is improved, and local convergence of the optimization algorithm is effectively avoided; and when the designed arrayantenna is applied to a large-scale radar system, the radar has the advantages of long operating distance, high resolution, low cost, light weight, high engineering realizability and the like, and isworthy of popularization and application.

Description

technical field [0001] The invention relates to the field of array antenna design, in particular to a method and system for discretized area scanning sub-array-level sparse optimization. Background technique [0002] With the development of radar detection and reconnaissance technology, the demand for radar working distance is gradually increasing. The antenna is responsible for the transmission and reception of radar signals, and the demand for its gain makes the aperture area of ​​the antenna array larger and larger. In the antenna array design, a large number of antenna elements and T / R components are required when large-scale radars are arranged at equal intervals, and the production cost is high. In order to reduce the number of antenna elements and channels, sparse array design has been developed rapidly. Genetic algorithm, particle swarm algorithm, and simulated annealing algorithm can find the optimal solution through iteration and evolution. In order to avoid fall...

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Problems solved by technology

[0004] In the existing position point generation scheme, all array elements can be randomly arranged in any position in the target area, and the minimum distance between any two points is judged. When the distance between any two points meets the distance constraint condition, the group of position points is valid point group; but when the limit distance is large or the number of array elements is large, the minimum distance between two points in the generated position point group is difficult to meet the distance restriction condition, resulting in most of the generated point groups being invalid point groups. resulting in low optimization efficiency

In addition, there is a time-sharing generation point group scheme. After generating position points one by one and removing the current arrangement area, it can ensure that the generated points will not overlap. However, this method has many times of judgment and low optimization efficiency; the scheme of generating point groups at the same time High efficiency, but when the number of array elements is large, the probability of generating effective point groups is very low, and it is not possible to realize engineering for ultra-large-scale arrays

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