基于机载光电平台的目标跟踪与轨迹预测算法

A Target Tracking and Trajectory Prediction Algorithm Based on Airborne Optoelectronic Platform

针对利用机载光电平台进行"空对空"目标跟踪时,跟踪效果受环境影响较大且全遮挡情况下目标容易跟丢的问题,在传统Cam Shift算法的基础上,提出了一种动态的基于多特征融合与相对Kalman模型的目标跟踪与轨迹预测算法。采用融合颜色、纹理、梯度特征的方式构建目标模板,提高了模型的描述能力;跟踪过程中引入特征模板动态更新环节,保证了算法的长期稳定性;在全遮挡的情况下,利用背景中心点以及飞行目标与该中心点的差值分别构建Kalman模型,并采用二次遮挡判断方法,大大降低了误判和丢帧概率。实验结果表明,所提算法具有较高的准确性、实时性与稳定性。

When using airborne optoelectronic platforms for 'air-to-air'target tracking, the tracking effect is always affected by the environment and it is easy to lose the completely-sheltered target. Based on the traditional CamShift algorithm, a dynamic target tracking and trajectory prediction algorithm based on multifeature fusion and relative Kalman model is proposed. In target template construction, the method of fusing the color, texture and gradient features is adopted to improve model description. During the tracking period,the dynamic update of feature templates is introduced to ensure the long-term stability of the algorithm. In the case of complete occlusion, the Kalman models are established respectively for background center and the difference between the flight target and the center point through the secondary occlusion judgment method, which greatly decreases the probability of misjudgment and frame loss. Experimental results show that the proposed algorithm has high accuracy, fine real-time performance and stability.