2D non-linear state optimization using evolutionary techniques

In modelling the non-Gaussian and non-linearity behaviour in t he systems accurately for the estimation of the density 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 Bayesian 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 (Biogeography based optimization) and PF-PSO (Particle swarm optimization. The efficacy of particle filter is expressed in the form of root-mean-square-error values (RMSE) and show the improved estimation accuracy of PF-BBO over the PF-PSO.