Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization

The standard particle filter (PF) algorithm has the issue of particle diversity loss caused by particle degradation and resampling, which makes it impossible for particle samples to accurately represent the true distribution of state probability density function. Particle swarm optimization (PSO) algorithm can effectively improve the particle degradation problem of particle filter namely, PSO-PF, but its fitness function is greatly affected by the variance of measurement noise, and is easy to fall into local optimal, which greatly limits the filtering accuracy. Therefore, this paper proposes an algorithm that combines genetic algorithm (GA) and PSO algorithm to improve particle filtering, namely, GA-PSO-PF. This algorithm combines the fast convergence speed of particle swarm optimization with the strong global searching ability of genetic algorithm to increase the diversity of particles while ensuring the effectiveness of superior particles, and improve the speed and accuracy of finding the optimal solution. Experimental results show that the filtering performance of the proposed algorithm is better than PF and PSO-PF, and the positioning and tracking accuracy is improved by 54.44% compared with PF and 27.20% compared with PSO-PF. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Identification, Information and Knowledge in the internet of Things, 2020.