光电跟踪系统中高频测角信息的攫取研究

为了能够充分利用光电跟踪设备相邻两次激光测距间隔内的高频测角信息，以提高目标跟踪精度，给出了一种航迹“融合”式目标状态估计方法。该方法借助扩展Kalman滤波算法．将无测距信息时刻的目标测角信息代入独立的滤波器，而对有距离时刻的目标测量信息建立另外的滤波器，然后将两滤波器的输出结果进行加权融合，从而在不增加任何硬件成本的前提下，进一步提高了具有激光测距的光电跟踪系统的目标状态估计精度。对模拟数据和靶场实测数据的测试结果表明了该方法的可行性和有效性。

Data Mining of Angle Measurements with High Repetition Frequency in Photoelectric Tracking System

To make full use of the angle measurements between the intervals of two adjacent rang measurements from photoelectric tracking equipment and to improve the target tracking precision,a track fusion estimation method is proposed in this paper.By using extended Kalman filter(EKF),the angle measurements at the moment without rang measurement are inputted to a filter,and the measurements at the moment which have rang measurement are inputted to another filter,then target state estimation is fused by the outputs of the two filters.The proposed method can obviously improve the target state estimation precision of photoelectric tracking equipment with laser ranging without increasing any hardware cost.Computer simulation results on simulation data and target field measurements show that the proposed algorithm is feasible and effective.