基于改进灰狼算法的粒子滤波算法研究

针对传统粒子滤波算法易出现粒子贫化与权值退化现象和为了实现对非线性系统较为准确的状态估计,通常需要大量粒子的参与的问题,提出了基于改进灰狼算法的新型粒子滤波方法,该算法用粒子表征灰狼个体,模拟狼群捕猎的过程,使粒子向后验概率的高似然区域移动,提高粒子分布的合理性。在灰狼寻优算法中引入了莱维飞行策略,提高灰狼算法的收敛速度;在部分重采样前采用了权值自适应调整策略,增加粒子的多样性。仿真实验结果表明:改进的方法提高了粒子滤波的估计精度、保证了粒子的多样性与粒子分布的合理性、降低了状态估计所需的粒子数量。

Research on Particle Filter Algorithm Based on Improved Grey Wolf Algorithm

The traditional particle filter algorithm is prone to particle depletion and weight degradation, and a large number of particles are needed to make an accurate estimation of non-linear system. A novel particle filter algorithm based on grey wolf optimization algorithm is proposed. The particles are employed to characterize the individuals of grey wolf and simulate the process of wolf hunting, which makes the particles move to the high likelihood region of posterior probability and improve the rationality of the distribution of the particles. Levy flight strategy is introduced in grey wolf algorithm to improve the convergence speed. The adaptive weight adjustment is adopted before partial resampling to increase the diversity of the particles. The results of simulation experiment show that the improved algorithm upgrades the estimation accuracy of particle filter, ensures the diversity of particles and the rationality of particle distribution, and reduces the number of particles required for state estimation.