Particle estimation algorithm using Sh correlation coefficient for nonlinear system state

This paper introduces a particle estimation algorithm using Sh correlation coefficient (PE) for nonlinear system state. It consists of prediction, update, and smoothing. It modifies the weights of the particles using the Sh correlation coefficient between the observations of the estimated state and the observations of the particles. The simulation results are presented to demonstrate the improved performance of the SCPF over those known particle filters including the sequential importance resampling algorithm, the auxiliary particle filter, the regularized particle filter, the Gaussian particle filter, and the Gaussian sum particle filter.