Array sparse optimization method based on adaptive genetic algorithm

The invention provides a sparse array optimization method based on an adaptive genetic algorithm in the technical field of radar reconnaissance array optimization. The method comprises the following steps of optimizing a model by proposing an array, encoding arrays, then, calculating a fitness value by utilizing a Cramer-Rao bound; selecting and crossly mutating the generated individuals by utilizing the fitness value, correspondingly changing crossover probability and mutation probability along with the increase of iteration times, finally optimizing to obtain the optimal individual, and taking the sparse array placement mode corresponding to the optimal individual as the optimal placement mode of array elements in the radar array. The method is a sparse array optimization method based ona self-adaptive genetic algorithm, an array element selection mode mainly takes the maximum array aperture and the minimum array element spacing as constraint conditions, takes a Cramer-Rao bound asa fitness function, and ensures that the array has relatively high direction finding performance under the conditions of the same array element number and the same minimum array element spacing.