The application of SRCQMMSPF in ballistic reentry target trajectory tracking

Abstract

Ballistic reentry target trajectory tracking is an important application of nonlinear filtering problems. Under non-Gaussian noise, Standard Particle Filter (SPF) and many revised PFs (i.e. MSRCQPF) can obtain good results. However, the two algorithms may meet some defects respectively, such as particle degradation, large computational complexity and so on. This paper propose a multimode sampling particle filter algorithm that based on square-root cubature quadrature Kalman filter (SRCQMMSPF). The proposed algorithm uses the estimated value of square root cubature quadrature Kalman filter as a basic proposal distribution for multimode sampling particle filter. Simulation results that when the measurement noise of ballistic reentry target trajectory tracking model is glint noise, compared with SPF algorithm and MSRCQPF algorithm, the SRCQMMSPF algorithm not only has a low computational complexity, but also has very good tracking accuracy. Especially in the case of ballistic target maneuver, the proposed algorithm can obtain higher estimation accuracy at lower computational cost. Besides, in the SRCQMMSPF algorithm, the high order approximation of Chebyshev polynomial can also improve the filtering accuracy.