关键词: 波达方位估计, 粒子群算法, 随机最大似然算法, 计算复杂度, 假设技术

Abstract: Aiming at the problem of large computational complexity due to multidimensional nonlinear optimization in the DOA estimation of the random maximum likelihood algorithm, a SML algorithm is proposed to limit the search space of particle swarm optimization algorithm. This algorithm overcomes a defect that when the ESPRIT algorithm defines the initial space of PSO, the signal cannot be directly processed by ESPRIT algorithm when the array structure is a non-uniform linear array and the signal is a coherent signal, and we need to adopt a set of preprocessing techniques, which increases the complexity of algorithm calculation. The key point of the proposed algorithm is to use the hypothesis technique to determine the initialization point instead of the solution of the ESPRIT algorithm, the initial solution space of the PSO algorithm is determined by combining the CRRAM. This method eliminates the need for preprocessing techniques, and the algorithm that defines the PSO initialization space greatly reduces the computational complexity of the SML algorithm. Experimental results show that the proposed algorithm provides fairly good initial values for both coherent and incoherent cases. Finally, the proposed algorithm is compared with many existing algorithms, and the validity and accuracy of the proposed algorithm are verified.

Key words: direction-of-arrival estimation, particle swarm optimization algorithm, stochastic maximum likelihood algorithm, computational complexity, hypothesis technique