为提高机载环境对地面强机动性目标跟踪的鲁棒性, 本文以粒子滤波为跟踪框架, 研究了它的动态模型与观测模型。针对机载环境的特点与跟踪目标的强机动性, 提出了基于Kristan双步动态模型结构的加速度双步动态模型(TSA)。根据Yilmaz等人提出的非对称核函数思想, 针对实际工程中目标变化特点与实时性要求, 提出利用Snake算法提取目标轮廓, 以轮廓信息构造非对称核函数的方法。最后, 依据上述方法提出了TSA-AK粒子滤波跟踪算法。利用提出的算法对机载环境对地目标跟踪的视频进行了测试, 结果表明, 本文算法可实现对大幅度变速运动目标的稳定跟踪, 正确跟踪率为98%; 对大小为25 pixel×30 pixel的目标的处理帧率为26 frame/s。

To improve the robustness while tracking a ground target with strong mobility in the cabin environment, a particle filter was taken as tracking framework, and its dynamic model and observation model were investigated. According to the two-stage dynamic model proposed by Kristan et al, a two-stage acceleration (TSA) dynamic model was proposed for the characteristics of cabin environment and the strong mobility of the tracking target. According to the idea of asymmetric kernel function proposed by Yilmaz et al., a method was proposed by using Snake algorithm to extract the object contour and to construct an asymmetric kernel function based on contour information to solve the real-time moving target problem. Finally, the TSA-AK particle filter tracking algorithm was proposed based on above methods. The proposed algorithm was tested on the video tracking ground target in cabin environment. The results show that the proposed algorithm can stably track target moving in a wide range of velocity. The targeting accuracy is 98%, and the computing frame rate is 26 frame/s when the object scale is 25 pixel×30 pixel.