基于新型粒子群优化的粒子滤波雷达目标跟踪算法

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.

A Particle Filter Radar Target Tracking Algorithm Based on Novel Particle Swarm Optimization

Particle filter based on particle swarm optimization algorithm is not precise and easily traps in local optimum,and it is difficult to satisfy the requirement of target tracking.To solve these problems,a novel particle swarm optimized particle filter is proposed.The method for updating particles is optimized by analyzing the cognition rule of individuals to groups,and the speed update strategy is improved.As a result,the superior particle velocity can mutation with a small probability,which improves the search ability.Meanwhile,due to the random evaluation for inferior particle,the diversity of filter is ensured.The simulation results show that this algorithm has the high precision,strong robustness and it’s suitable for radar target tracking.