基于数据链和光电传感器的融合跟踪

摘要

数据链是战争中信息互通、资源共享的生命线,研究了基于数据链和光电传感器的融合跟踪方法.根据数据链和光电传感器数据的不同特点,对数据链数据应用改进的交互式当前统计模型滤波外推算法进行时间配准,并根据光电传感器测量角度信息的非线性应用交互式卷积粒子( IMM-CPF)滤波算法进行融合跟踪.使用IMM-CPF和IMM -EKF算法对融合跟踪进行对比仿真,仿真结果表明,使用IMM-CPF算法的数据链和光电传感器的信息融合对目标的跟踪达到了很好的效果.%The fusion tracking based on data-link and photoelectric sensor was developed as a better method for passive target tracking. According to the different characteristics of data-link and photoelectric sensor, the improved interacting current statistical mode filter extrapolation was used on the data of data-link for time registration. The interacting multiple model convolution particle filter (IMM-CPF) was used for fusion tracking because of the nonlinear feature of photoelectric sensor's angle information. Computer simulation was made by using the IMM-CPF and EKF algorithm, and the result showed that fusion tracking by using the IMM-CPF algorithm based on data-link and photoelectric sensor is effective for the target tracking.