New particle filter for nonlinear filtering problems

Particle filters have gained special attention of researchers in various fields. The key idea of this technique is to represent the posterior density by sets of weighed samples. This paper proposes a new particle filter which is based on the extended Kalman filter and the Unscented Kalman filter. It first uses the former to generate an estimate of the state at time k, and then uses the latter to repeat the process and to gain the final estimate of the state and corresponding covariance at time k. In the experiments, the authors test five different particle filters on two different nonlinear systems. The experimental results indicate that the proposed particle filter has much better performance than the other four particle filters do.