Adaptive unscented particle filter for nonlinear statement estimation

The unscented particle filter (UPF) is well known as a state estimation method for nonlinear system. However, UPF has the inherent drawback of costly calculation. In this paper, an adaptive unscented particle filter by online change the number of particles is proposed to overcome the drawback of computational burden in the traditional unscented particle filter. Based on the K-L distance sampling, the new algorithm calculates the number of particles in the next deviation according to the predicted particles in the state space. Then the computer simulations are performed to compare the proposed algorithm and other state prediction and estimation methods, such as UPF and particle filter. The simulation results demonstrated that the adaptive UPF is very efficient and smaller time consumption compared to traditional unscented particle filter. Therefore the adaptive UPF is more suitable to the nonlinear statement estimation.