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针对基于粒子群优化算法的粒子滤波精度不高,容易陷入局部最优,难以满足目标跟踪的问题,提出了一种新的粒子群优化粒子滤波算法,该算法利用社会个体对群体的认知规律优化了粒子更新的方法,并且完善了粒子速度的更新策略,使优势速度有较小概率变异,从而提高了寻优能力,同时将劣势速度随机初始化,保证了样本的多样性.实验结果表明,该算法精度高,鲁棒性强,可以有效地应用于雷达机动目标跟踪.
Aiming at the problem that the particle filter based on Particle Swarm Optimization (PSO) algorithm is not accurate enough, it is easy to fall into the local optimum and difficult to meet the target tracking problem. A new Particle Swarm Optimization Particle Filter (PSO) algorithm is proposed, which uses the social cognition The method of particle renewal is optimized, and the update strategy of particle velocity is improved, which leads to a smaller probability mutation of the dominant velocity, which improves the optimization ability and initializes the inferior velocity randomly, which ensures the diversity of the samples.Experimental results show that, The algorithm has high accuracy and robustness and can be effectively applied to radar maneuvering target tracking.