Direction finding based on cat swarm optimization for bistatic MIMO radar
In the paper, the maximum-likelihood (ML) algorithm is employed to resolve the difficulty of direction finding for bistatic multiple-input multiple-output (MIMO) radar. In order to obtain the global optimal solution of ML algorithm, the cat swarm optimization (CSO) is used to solve the ML equation, which has the advantages of low computational complexity and fast convergence speed. Thus, a novel direction finding approach called CSO-ML is proposed, and it is effective to find coherent and independent signal sources for bistatic MIMO radar. Monte-Carlo simulations show that the proposed CSO-ML can obtain higher accuracy and success rate of estimation compared with previous classical direction finding approaches of bistatic MIMO radar.