Autonomous Systems and Intelligent Control International Joint Research Center, Xi'An Technological University, Xi'an 710021, China.
School of Mechatronic Engineering, Xi'An Technological University, Xi'an 710021, China.
Sensors (Basel). 2018 Nov 6;18(11):3791. doi: 10.3390/s18113791.
To deal with the problem of multitarget tracking with measurement origin uncertainty, the paper presents a multitarget tracking algorithm based on Adaptive Network Graph Segmentation (ANGS). The multitarget tracking is firstly formulated as an Integer Programming problem for finding the maximum a posterior probability in a cost flow network. Then, a network structure is partitioned using an Adaptive Spectral Clustering algorithm based on the Nyström Method. In order to obtain the global optimal solution, the parallel A* search algorithm is used to process each sub-network. Moreover, the trajectory set is extracted by the Track Mosaic technique and Rauch⁻Tung⁻Striebel (RTS) smoother. Finally, the simulation results achieved for different clutter intensity indicate that the proposed algorithm has better tracking accuracy and robustness compared with the A* search algorithm, the successive shortest-path (SSP) algorithm and the shortest path faster (SPFA) algorithm.
为了解决测量原点不确定的多目标跟踪问题，本文提出了一种基于自适应网络图分割（ANGS）的多目标跟踪算法。多目标跟踪首先被公式化为在费用流网络中寻找最大后验概率的整数规划问题。然后，基于 Nyström 方法使用自适应谱聚类算法对网络结构进行分割。为了获得全局最优解，使用并行 A搜索算法处理每个子网。此外，通过轨迹镶嵌技术和 Rauch-Tung-Striebel（RTS）平滑器提取轨迹集。最后，针对不同的杂波强度进行的仿真结果表明，与 A搜索算法、连续最短路径（SSP）算法和最短路径更快（SPFA）算法相比，所提出的算法具有更好的跟踪精度和鲁棒性。
