2D non-linear state optimization using evolutionary techniques

Abstract

In modelling the non-Gaussian and non linearit y behaviour in t he s y stems accuratel y for t he estimation of the densit y function, Particle filter(PF) is considered more precise compared to other filters like Kalman filter. Particle filters are also known as Sequential Monte Carlo method which used the sampling method in implementing the recursive Ba y esian filters. However, Particle filter has limitations like the degradation of particles and sample impoverishment (SI) which afford an immense challenge in the non-linear state estimation of particles. In order to triumph over the limitations (SI), in this paper, we present novel implementation of 2-D state estimation of particles based on bearing on tracking problem using PF-BBO (Biogeograph y based optimization) and PF-PSO (Particle swarm optimization. The efficac y of particle filter is expressed in the form of root-mean-square-error values (RMSE) and show the improved estimation accurac y of PF-BBO over the PF PSO.