A Novel Immune Genetic Algorithm for Signal Phase Matching Principle

Due to the large computational load of the signal phase matching (SPM) principle for DOA estimation, a novel immune genetic algorithm (NIGA) was proposed to search the optimal solutions of the singular value decomposition based SPM direction finding algorithm (SVDSPM). The proposed algorithm used the two-individual mean information entropy for the immune selection, assigned the different weight to each term of the total information entropy at the same loci in a pair of individuals, and constructed a better selection scheme to ensures more various individuals for preserving the diversity of the population. Meanwhile, the energy function of the SVDSPM method was stretched by the simulated annealing (SA) to construct the new fitness function. Simulation results show the algorithm in this paper performs well in terms of the quality of solution and computational cost, and its stability and accuracy are enough to implement the high-resolution DOA estimation at lower SNR.