舰载光电跟踪设备在跟踪百公里以上的目标时, 由于受到障碍物干扰,目标有时可能从视场中丢失, 需采用记忆跟踪算法对目标的未来时刻位置进行预测, 重新找回目标。常规的CA、CV模型预测目标时忽略了残差, 记忆跟踪时间短, 从而造成预测目标不够精确。针对以上问题, 提出了Kalman目标预测模型, 延长记忆跟踪时间。首先, 由船地坐标转换公式推导了甲板坐标系下船摇速度, 前馈到伺服控制系统速度回路中, 保证视轴自稳定, 同时提高跟踪精度; 其次, 概述了CA、CV、Kalman目标预测模型; 最后, 重点论述了3种目标预测模型记忆跟踪和实时雷达引导二维位置信息之间的关系。试验结果表明, 本文由于引入了Kalman目标预测模型, 使得记忆跟踪时间比传统的CA、CV模型的预测目标时间提高了一个数量级。解决了工程中舰载光电跟踪设备受船摇影响时跟踪精度低和记忆跟踪时间短的问题。

When shipboard photoelectric tracking equipment traces target more than 100 km away from it, because of disturbance of barrier, target is often lost from field of view.Under this condition, memory track algorithm shall be adopted to predict location of target at future time to re-find target. Conventional CA and CV models ignore residual error when predicting target, and memory track time is short, which causes insufficient accuracy of target predicted. In consideration of above problems, Kalman target prediction model was put forward to lengthen memory track time. Firstly, this paper derived boat-swing velocity under deck coordinate system from ship-earth coordinate transformation formula, performed feedforward to velocity loop of servo control system to guarantee self-stabilization of optical axis and improve tracking accuracy simultaneously; secondly, CA, CV and Kalman target prediction models were described; finally, 2-dimension position information relationship between memory tracking of 3 kinds of target prediction models and real-time radar guidance location was mainly discussed. Test results show that compared with traditional CA and CV models, target prediction time of proposed method is improved more than an order of magnitudes, that's because Kalman target prediction model is introduced in this paper. The problem in project that tracking accuracy of shipboard photoelectric tracking equipment will be low and memory tracking time will be short when it is affected by boat-swing is solved.