基于自适应人工鱼群的粒子滤波算法

针对粒子滤波算法中存在粒子退化及粒子枯竭的问题,本文提出一种自适应的人工鱼群粒子滤波算法,该算法通过把觅食行为和聚群行为引入粒子滤波算法中,并自适应调整人工鱼的移动步长和视野范围,从而增加了粒子的多样性,克服了粒子退化及粒子枯竭问题;驱动粒子向最优位置靠近,克服粒子易陷入局部最优问题,增强了粒子的全局搜索能力。仿真实验表明,本文提出的算法与人工鱼群粒子滤波及标准粒子滤波算法相比,滤波精度有显著的提高。

Particle Filter Algorithm based on Adaptive Artificial Fish School Algorithm

Aiming at the problems of particle degeneracy and particle impoverishment in particle filter,this paper proposed a new particle filter which based on adaptive artificial fish school algorithm(AAFSA-PF).The new algorithm introduced the foraging behavior and cluster behavior into particle filter,adjusted the step and the view of artificial fish adaptively.The new algorithm increased the diversity of particles,overcoming the problems of particle degeneracy and particle impoverishment,driving the particles move to the optimum area,overcame the problem that particles are easy to fall into local optimum area,enhanced the global search ability of particles.Simulation results show that,compare with the conventional particle filter and the existing artificial fish school particle filer,the new algorithm improve the estimation accuracy effectively.