A Particle Filtering Algorithm Based on Cubature Kalman Filter

摘要

Based on the Cubature Kalman filter algorithm (CKF) algorithm, we present a new particle filtering algorithm. To construct the importance density of samples, the importance density function is generated by a new framework, in which the state of each particle is predicted according to the concept of CKF. In this way, the new proposed method can take into account the most recent measurements, resulting in an accurate approximation to the nonlinear non-Gaussian system. Thus, we can get a great improvement in the estimation performance of the system. Finally, the effectiveness of the proposed model is validated through simulation.