Aiming at the need of coherent source direction of arrival estimation, combined with particle swarm optimization algorithm, this paper proposes a generalized maximum likelihood algorithm based on chaos adaptive mutation particle swarm optimization (CAMPSOGML). The algorithm has no constraints on the geometric structure of the array, and the number of resolved sources can be greater than the number of array elements. The algorithm introduces chaos initialization and adaptive mutation strategies into the particle swarm algorithm, which effectively improves the convergence speed and overcomes the disadvantage that the particle swarm algorithm is prone to fall into the local optimal value. Computer simulation shows that compared with the generalized maximum likelihood estimation method based on real genetic algorithm and particle swarm algorithm, the CAMPSOGML algorithm has advantages in convergence speed and estimation accuracy, and is a novel and effective decoherence algorithm.
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