A differential evolution algorithm-based uniform area array sparse optimization method

Abstract

The invention discloses a differential evolution algorithm-based uniform area array sparse optimization method. At present, a phased array radar sparse array optimization technology lacks differentialevolution algorithm optimization under a uniform area array model. The method comprises the following steps of 1, converting a real number code in a differential evolution algorithm into a binary code; 2, constructing a fitness function by using the sum of the planar peak value side lobe levels; and 3, drawing a sparse array element distribution map through the binary codes obtained after optimization, and further obtaining a three-dimensional directional diagram of the sparse array. According to the method, based on a differential evolution algorithm, an array aperture, the number of array elements and an array element interval are taken as constraint conditions, and the minimum plane peak sidelobe level is taken as an objective function, so that a sparse array element position distribution map and a sparse array three-dimensional directional diagram are obtained. Simulation results show that the method has the advantages that constraint conditions are met, the directional diagram does not have obvious grating lobes and obvious mutual coupling influence in a visual area, the performance is good, and the algorithm convergence speed is high.

Description

Technical field [0001] The invention belongs to the technical field of radar array optimization, and particularly relates to a design method for sparse optimization of a uniform area array. Background technique [0002] In radar and communication systems, with the widespread application of array antennas, the optimization of antenna array design has become an important part of it. The optimization of array antenna array technology is based on the study of the relationship between antenna array performance and array geometry. The purpose of optimizing the array structure is to obtain excellent performance indicators to meet the system's requirements for array antennas. [0003] Evenly spaced arrays (periodic arrays) have been extensively studied because of the equal spacing between adjacent array elements, the relatively simple array structure, and the relatively intuitive mathematical model. However, the uniform array has the following problem: in order to avoid the appearance of ...

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Problems solved by technology

However, the uniform array has the following problems: in order to avoid grating lobes, the uniform array element spacing should be guaranteed to be no greater than λ / 2 (λ is the radiation wavelength), but when the radiation wavelength is small, the array element spacing will decrease accordingly, then There will be a mutual coupling effect between the array elements, which will seriously affect the performance of the pattern; secondly, the main lobe width of the array antenna pattern is inversely proportional to the array aperture. In order to achieve a narrower main lobe width, it is necessary to increase the antenna unit. The design cost increases accordingly

Since Goldberg et al. applied the genetic algorithm to the sparse array, a large number of scholars have applied the genetic algorithm and its improved algorithm to the sparse array optimization, but the genetic algorithm has many control parameters, and the parameter setting has a great influence on the final result, and in practical application In , the number of sparse array elements is large, and the parameters need to be adjusted continuously. The algorithm is difficult to use, and the convergence speed is slow and unstable.

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