Yun Wu
ABSTRACT
The particle filter based on particle swarm optimization algorithm has low precision and is easy to fall into local optimization, which is difficult to meet the needs of target tracking. In this paper, a new particle swarm optimization particle filter algorithm is proposed. The algorithm designs adaptive inertia weight and adaptive learning factor to balance the global search ability and local search ability. Meanwhile, the mutation based on arithmetic crossover and the replacement of natural selection mechanism are proposed, which increases the diversity of particles and improves the convergence accuracy of the algorithm. Finally, Gaussian perturbation is added to make the particles vibrate and jump out of the local optimum more easily. Experimental results show that the algorithm has high accuracy and strong robustness, which can be effectively applied to radar maneuvering target tracking.
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