An IMM-VB Algorithm for Hypersonic Vehicle Tracking with Heavy Tailed Measurement Noise
Yun Peng, Panlong Wu, Shan He · 2018
In order to solve the problem of degraded tracking accuracy of hypersonic vehicle caused by outliers disturbance in real systems, an interactive multi-model variational Bayesian (IMM-VB) algorithm is proposed. Firstly, the algorithm obtains state prediction values and weights by IMM. Then the Student's t distribution with heavy tail characteristics is used to replace the Gaussian distribution to describe the measurement model. Finally, the measured covariance and the target state are estimated by VB. Simulation results show that the algorithm has higher tracking accuracy than the IMM algorithm under outliers observation conditions.