Abstract:

A new maximum likelihood direction-of-arrival (DOA) estimator based on ant colony optimization (ACOML) is proposed to reduce the computational complexity of multidimensional nonlinear existing in maximum likelihood (ML) DOA estimator. By extending the pheromone remaining process in the traditional ant colony optimization into a pheromone Gaussian kernel probability distribution function in continuous space, ant colony optimization is combined with maximum likelihood method to lighten computation burden. The simulations show that ACOML provides a similar performance to that achieved by the original ML method, but its computational cost is only 1/15 of ML.