Multi-sensor information fusion extended Kalman particle filter

Abstract

In this paper, a new extended Kalman particle filter based information fusion is proposed for state estimation problem of nonlinear and non-Gaussian systems. It uses extended Kalman filter algorithm to update particles in particle filter, with which the local state estimated values can be calculated. The multi-sensor information fusion filter is obtained by applying the standard linear minimum variance fusion rule weighted by scales. The simulation results show that the proposed algorithm improves the accuracy of filter compared with single sensor.