Maximum likelihood direction of arrival estimator based on modified ant colony optimization

A novel maximum likelihood direction of arrival estimator based on modified ant colony optimization is proposed to lighten the exponentially increasing computation burden introduced by multidimensional grid search. By integrating chaos initialization and local search, modified ant colony optimization can overcome the drawbacks of ant colony optimization, such as low convergence speed and being easily trapped in local optimum. It is shown via simulations that the proposed method can keep the excellent performance of the original maximum likelihood direction of arrival estimator and reduce the computation evidently. Due to the initialization via chaotic sequences and local search in the solution update procedure, the proposed method reduces the sensitivity of parameters, and thus outperforms the maximum likelihood estimator based on ant colony for its higher precision and less computation.