基于UT变换的自适应扩维IMM电扫描跟踪算法

Adaptive Augmented IMM Tracking Algorithm for Electronically Scanned Radars Based on Unscented Transformation

针对机动目标跟踪问题,采用扩维的匀速和转弯(ACV-2ACT)模型组成模型集,结合电扫描雷达波束灵活的特点,利用自适应调度算法,设计了一种基于UT变换的自适应扩维IMM算法。通过无味滤波器估计转弯率和状态,使模型集具有自调整能力。与EKF-AIMM算法进行仿真比较,结果表明在相同情况下,本算法有较好的稳定性,能更加有效节约雷达资源。

For maneuvering target tracking, a new augmented interacting multiple model（AIMM） technique based on unscented transformation is developed in this paper. The model set of this multiple model algorithm is established in terms of one augmented constant velocity（ACV） model and two augmented coordinated turn （ACT） models. Combined with an adaptive revisit concept, the method is applied to electronically scanned radars. The turn rates and the state estimation process are realized by unscented Kalman filter and they ensure significant self-adjusting capabilities. Compared with EKF-AIMM algorithm, it is shown that the approach possesses better stability, and can economize radar resource more efficiently.