Array sparse optimization method based on adaptive genetic algorithm
A technology of sparse optimization and genetic algorithm, applied in the field of radar reconnaissance array optimization, to achieve the effect of good direction finding performance and high direction finding performance
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2020-05-15
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Abstract
Description
technical field
[0001] The invention belongs to the technical field of radar reconnaissance array optimization, and particularly proposes an array sparse optimization method based on an adaptive genetic algorithm. The optimal sparse array obtained by the method optimization of the present invention constrains the maximum array aperture, the minimum Under the condition of the array element spacing, the array Cramereau limit value is the smallest, so that the direction finding accuracy is the highest during reconnaissance. Background technique
[0002] In practical applications, with the development of technology and the needs of operations, the passive receiving detection on the active phased array to complete the active and passive integration, and even the demand design of the passive radar detection system based on the phased array system have been regarded as Research hotspots. This makes it particularly important to select the array element antenna design for passive de...
Examples
Embodiment Construction
[0035] refer to figure 1 , is a flow chart of an array sparse optimization method based on an adaptive genetic algorithm of the present invention, wherein the array sparse optimization method based on an adaptive genetic algorithm includes the following steps:
[0036] Step 1. Determine the sparse optimization model of the planar array: change the position of the array to be sparsely optimized into a column of vectors, that is, an individual in the population represents an array to be sparsely optimized.
[0037] First, the position of each element of the array to be sparsely optimized is represented by a column of vectors, which is denoted as the individual to be sparsely f j,i (i=1,2,...,NP; j=1,2,...,L; L=M×M), where i represents the serial number of the individual in the corresponding population, L represents the number of full array elements, and M represents Number of array elements in azimuth and elevation, f j,i =1 means that there is an array element at the correspo...