Particle swarm optimization particle filter denoising algorithm with mutation operator

Abstract

In this paper, a novel particle filter (NPSO-PF) algorithm is proposed, which is called particle cluster optimization particle filter algorithm with mutation operator, which is used for real-time filtering and noise reduction of nonlinear vibration signals. Because of its introduction of mutation operator, this algorithm has no problem that particle swarm optimization (PSO) algorithm is easy to fall into local optimal value and the calculation accuracy is not high. At the same time, through the mutation operation, the distribution and diversity of particles in the sampling process are improved, and the particle filter (PF) algorithm is solved in which the particles are poor and the utilization rate is not high. The mutation control function makes the particle set optimization process in the early and late stages, and improves the convergence speed of the particle set, which greatly reduces the running time of the whole algorithm. Simulation experiments show that compared with PF algorithm and PSO-PF algorithm, the proposed new particle swarm optimization particle filter (NPSO-PF) algorithm has lower root mean square error, shorter running time, higher signal to noise ratio and more stable filtering performance. It is proved that the algorithm is suitable for real-time filtering and noise reduction processing of nonlinear signals.