基于光电跟踪系统的联合Kalman滤波器算法研究

Federated Kalman Filter Based on Optoelectronic Tracking System

传统光电跟踪系统中,采用单传感器方式进行目标跟踪存在跟踪不稳定及传感器切换时跟踪精度大幅度下降等问题。利用融合光电跟踪系统多个传感器信息可以提高系统性能的特点,研究了采用联合Kalman滤波器的融合算法。首先对各传感器获取的目标轨迹数据进行局部滤波,然后由全局滤波器对各局部滤波器的目标轨迹估计进行融合处理,得到目标状态估计的更新。实验仿真结果表明,该融合算法实现了目标的稳定跟踪,其跟踪精度高于传统单传感器跟踪模式,并且有效地解决了传感器失效造成的跟踪精度严重下降问题。

Traditional optoelectronic tracking system mainly relies on single sensor. It brings on unstable tracking performance and dramatic decrease of tracking precision while switching among different sensors. A federated Kalman filter algorithm based on multi-sensor fusion of several sensors in optoelectronic tracking system is proposed. The local filters deal with target tracks from each sensors, then the synthetic target estimation is made according to the sensor fusion of local estimation. Experiments show that the proposed algorithm is effective in optoelectronic tracking system especially in the situation of switching among different sensors, and the precision is also superior to the traditional single sensor tracking algorithm.