PERFORMANCE ANALYSIS OF KALMAN-BASED FILTERS AND PARTICLE FILTERS FOR NON-LINEAR/NON-GAUSSIAN BAYESIAN TRACKING

摘要

In this paper, we present an overview performance analysis of Kalman- based filters and particle filters for Non-Linear/Non-Gaussian Bayesian tracking. The simulation results show that the particle filters have superior performance than the Kalman-based filters. Although the particle filters are time consuming, but in many situations such as the low data rate, low signal-to-noise ratio situations, the superior performance is very attractive.