Genetic algorithm based maximum likelihood DOA estimation

The Maximum Likelihood (ML) direction-of-arrival (DOA) estimation method was one of the first to be investigated. For a long time, the complexity and computational load of maximizing the multivariable, highly nonlinear likelihood function prevented it from popular. In this paper, we present the genetic algorithm (GA) for computing exact solutions to the likelihood function with almost guarantee of global convergence. The performance of GA-based ML and Multiple Signal Classification (MUSIC) algorithm have been compared for a variety of scenarios of SNR, DOA separation, number of snapshots, and computational cost. The relationship between ML technique and MUSIC is also investigated.