Iterated cubature Kalman particle filter algorithm

In order to improve particle impoverishment and sample size degeneracy of the standard particle filter (PF) algorithm, a novel iterated cubature Kalman particle filter based on the Gauss-Newton iteration method is proposed. The new algorithm directly uses the cubature rule-based numerical integration method to calculate the mean and covariance of the nonlinear random function by a set of the certain particles and their weights. Meanwhile, the Gauss-Newton iteration method is used to solve the least square problem of the cubature Kalman filter (CKF), thereby reducing linearization error and generating the importance density function of the algorithm. The importance density function of the new algorithm that incorporates the latest observations is more close to the system state posterior probability density. So the filter performance is improved. Simulation results show that compared with the PF and the CPF, the iterated Cubature Kalman particle filter method has a higher estimation accuracy.