Maneuvering Target Tracking Based on An Adaptive Interactive Multiple Model Box Particle Filter Under Interval Measurement

Aiming at the shortcomings of model switching and tracking accuracy of the existing interactive multiple model box particle filter (IMMBPF) in the interval measurement target tracking, an adaptive IMMBPF (AIMMBPF) algorithm is proposed, which uses posteriori information to construct a correction factor and modifies the Markov probability transition matrix. The probability of matching model increases rapidly, and the influence of mismatching model can be reduced. The simulation results show that the AIMMBPF algorithm has better model matching degree and target tracking accuracy than the original IMMBPF in the application of interval measurement maneuvering target tracking.