基于自适应滑动窗均值OSPA航迹关联算法

Track Fusion Based on the Mean OSPA Distance with an Adaptive Sliding Window

多目标航迹发生合并和分叉情况在实际工程中比较常见,给航迹关联造成很大困难.本文提出基于自适应滑动窗均值OSPA(Optimal Sub-Pattern Assignment)距离航迹关联方法,给出自适应航迹关联方法的关联矩阵.该方法可以自适应地给出历史航迹对当前航迹的作用影响,进而达到有效且准确地判断多目标航迹合并和分叉情况.最后仿真结果给出了两个传感器和两个目标形成的航迹关联,本文所提自适应关联方法和传统关联方法的关联比较,证明了本文所提方法的有效性,特别是在航迹合并和分叉情况下同样有效.

The combination and divergence of multi-target tracks are very common in the engineering practice,which make it more difficult for the track fusion. In this paper,a novel track fusion method with an adaptive sliding mean windowis proposed. Meanwhile,the adaptive associated matrix of proposed method is also given here. The proposed method could adaptively measure the effect of history track on the current track for the multi-target,thus judge the track combination and divergence,effectively and accurately. Furthermore,the simulations are performed with two sensors and two targets,have also shown that the proposed method is valid and stable for the track fusion,especially to the track combination and divergence.