A robust H∞ fusion tracking algorithm is derived to track a maneuvering air target based on the current statistical model, this tracking model is established by using the multiple stations passive infrared search and tracking (IRST) system. The robust H∞ fusion tracking algorithm is based on the combination of H∞ filter algorithm and central fusion tracking structure. This fusion tracking algorithm can solve the observability and nonlinear problems of the passive target tracking system simultaneously. The performance of the robust H∞ fusion tracking algorithm is verified by simulating the scenario of tracking a highly maneuvering air target by using three IR observations. The simulation results show that the robust H∞ fusion tracking algorithm has higher tracking precision than traditional extended Kalman filter (EKF). The robust H∞ fusion tracking algorithm is an effective tracking algorithm for IRST systems.%建立了机动目标的多站被动红外搜索与跟踪(infrared search and tracking,IRST)系统的当前统计模型,基于该模型提出了机动目标跟踪的鲁棒H∞融合滤波算法.该算法将H∞滤渡算法和集中融合跟踪算法相结合,对多站IRST测得的目标角度信息进行融合,可解决被动式跟踪系统的可观测性及非线性问题,以实现对目标较高精度的定位和跟踪.以三个观测站进行跟踪为例,对一个高机动目标进行了仿真研究,仿真结果表明,该滤波算法比扩展卡尔曼滤波(extended Kalman filter,EKF)算法有更高的跟踪性能,是IRST系统中一种有效的跟踪算法.
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