改进的邻近目标GM-PHD跟踪算法
Improved GM-PHD tracking algorithm for neighbor targets
池桂林 1胡磊力 2周德召2
作者信息
- 1. 光电控制技术重点实验室,河南 洛阳 471000;中国航空工业集团公司洛阳电光设备研究所,河南 洛阳 471000
- 2. 中国航空工业集团公司洛阳电光设备研究所,河南 洛阳 471000
- 折叠
摘要
针对目标跟踪系统在邻近目标场景下难以进行精确估计的问题,提出一种改进的邻近目标GM-PHD跟踪算法.该算法通过构建基于预测权值和速度参数的自适应门限,有效避免了杂波对算法更新步骤带来的巨大迭代负担.同时,我们充分考虑了目标邻近时量测的可能分布情况,针对目标与量测的"一对零"和"一对多"现象,提出了一种新的权重分配修正方法.结果表明,目标邻近时,改进后的算法在目标数和目标状态估计方面均优于传统算法,能够显著提高跟踪准确度.
Abstract
Aiming at the problem that the target tracking system is difficult to make accurate estimation in the neighboring target scenario,an improved neighboring target GM-PHD tracking algorithm is proposed.The algorithm effectively avoids the huge iterative burden of clutter on the update step of the algorithm by constructing an adaptive threshold based on the predicted weights and velocity parameters.At the same time,we fully consider the possible distribution of the measurements when the target is in the vicinity,and propose a new correction method of weight allocation for the phenomena of"one-to-zero"and"one-to-many"between the target and the measurements.The results of simulation experiments show that the improved algorithm outperforms the traditional algorithm in terms of target number and target state estimation when the target is neighboring,and can significantly improve the tracking accuracy.
关键词
多目标跟踪/概率假设密度/权值重分配/邻近目标跟踪Key words
multi-target tracking/probability hypothesis density/weight reallocation/adjacent target tracking引用本文复制引用
出版年
2024
NETL
NSTL
万方数据