Abstract: Aiming at the problem of the robustness of EKF-IMM is below average, an interacting multiple models algorithm using Strong Tracking Filter（STF） is proposed. Through introducing a fading factor of strong tracking filter, this algorithm realizes the realtime adjusting the gain of the filters, and updating the adaptive tracking performance and tracking precision for maneuvering targets accordingly. The Monte Carlo simulation result shows that this algorithm has the same tracking effect for non-maneuvering target as EKF-IMM, and the tracking performance for maneuvering target is superior to EKF-IMM on radial velocity and azimuth. The simulation results verify that this algorithm has better performance than EKF-IMM in tracking maneuvering targets.

Key words: maneuvering target tracking, Interacting Mutiple Model（IMM）, Strong Tracking Filter（STF）, fading factor, ACS model

摘要： 针对传统的EKF-IMM算法鲁棒性较差等问题，提出了一种基于强跟踪滤波器（STF）的交互式多模型算法。该算法通过引入强跟踪滤波器（STF）的渐消因子，实现了对滤波器增益的实时调节，从而提高了系统对机动目标的自适应跟踪能力和跟踪精度。仿真结果表明，在目标不发生机动时，该算法和EKF-IMM算法的跟踪效果相近，在目标发生强机动时，该算法在径向速度和方位角的跟踪精度要优于EKF-IMM算法；提出的算法具有更优的机动目标跟踪性能。

关键词: 机动目标跟踪, 交互式多模型, 强跟踪滤波器, 渐消因子, 自适应&ldquo, 当前&rdquo, 统计模型