Square-Root Recursive Update Gaussian Particle Filter

Abstract

For the construction of importance density function (IDF) of Gaussian particle filter, recursive update Gaussian filter (RUGF) which can effectively overcome the limitation of linear minimum mean square error criterion, updates the target state incrementally based on the gradient of nonlinear measurement function. Consequently, the posterior state estimation that is closer to the real distribution is obtained, but non-positive definite state covariance matrix will lead to recursive interruption. To solve this problem, the square-root implementation strategy of RUGF is firstly analyzed and then square-root recursive update Gaussian filter (SR-RUGF) is implemented by using cubature Kalman filter. Based on that, SR-RUGF is used to construct IDF for Gaussian particle filter. Simulation results demonstrate that the proposed algorithm can effectively solve the recursive interruption problem and obtain estimation result with higher accuracy.