Tracking Algorithm Based on Improved Interacting Multiple Model Particle Filter

Measurements are always interfered with glint noise in a radar target tracking system, which makes the performance of traditional filtering fall sharply and even divergent. Against this problem, a new Interactive Multiple Model Particle Filter(IMMPF) algorithm is proposed for target tracking by introducing PF into Interactive Multiple Model(IMM). Different from the general method to select importance density function from PF, the particles are extracted from observation likelihood function within depending on observation noises. Observation noise is modelled, and the latest observation is fused, then the target can be effectively tracked. Finally, the optimized method is simulated with respect to bearings-only tracking of maneuvering target in a glint noise environment. Compared with the existing filtering algorithms, it turns out that the developed filtering algorithm is more efficient and closer to the real-time tracking requirement of high maneuvering targets.