Improved Interactive Multiple Models Based on Self-Adaptive Turn Model for Maneuvering Target Tracking

Tracking maneuvering target is a challenging problem and Interactive Multiple Model (IMM) is proved an effective solution for it. In multiple model, the constant turn model (CT) is usually used to describe the target's turning motion. However, fixed or partially adaptive turn angular rate μ is usually adopted in CT which leads to tracking accuracy decrease. In this paper, an improved interactive multiple model set based on self-adaptive CT model is proposed. In self-adaptive CT model, the value of the turn angular rate ωis calculated based on both x and y velocity instead of only one of them or fixed value. To verify the improvement, particle filter, which is proved an effective way to solve non Gaussian nonlinear problem, is used to track maneuvering target. The performance of the proposed multiple model set is verified in two different scenarios and compared to two widely used multiple model sets. Simulation results show that the proposed model set has better performance both in tracking accuracy and computational cost.