Process noise identification based particle filter: an efficient method to track highly manoeuvring targets

In this study, a novel method, process noise identification-based particle filter is proposed for tracking highly manoeuvring target. In the proposed method, the equivalent-noise approach is adopted, which converts the problem of manoeuvring target tracking to that of state estimation in the presence of non-stationary process noise with unknown statistics. A novel method for identifying the non-stationary process noise is proposed in the particle filter framework. Compared with the multiple model approaches for manoeuvring target tracking, the proposed method needs to know neither the possible multiple models nor the transition probability matrices. One simple dynamic model is adopted during the whole tracking process. The proposed method is especially suitable for tracking highly manoeuvring target because of its capability of dealing with sample impoverishment, which is a common problem in particle filter and becomes serious when tracking large uncertain dynamics.