多帧高斯混合概率假设密度的多目标跟踪算法
发布时间：2018-10-08 16:29
【摘要】：针对低检测概率下多目标跟踪时,概率假设密度滤波器难以正确估计当前目标个数以及目标状态问题,提出一种基于多帧融合的高斯混合概率假设密度滤波算法。根据不同时刻目标权值构造目标多帧权值记录集及目标状态抽取标志。当某些时刻目标被漏检时,依据目标状态抽取标志,并结合目标多帧权值记录集中权值信息估计丢失目标的状态。仿真实验表明,算法有效地提高了低检测概率下现有相关算法的目标状态和数目估计精度。
[Abstract]:In order to solve the problem that it is difficult for the probability assumption density filter to estimate the number of targets and the state of the target under low detection probability, a new algorithm of Gao Si hybrid probability assumption density filter based on multi-frame fusion is proposed. According to the target weights at different times, the target multi-frame weight record set and the target state extraction mark are constructed. When the target is missed at some time, the state of the lost target is estimated by extracting the target state and combining the weight information of the target multiple frame weight record set. Simulation results show that the algorithm can effectively improve the target state and number estimation accuracy of the existing algorithms under low detection probability.
【作者单位】： 商丘职业技术学院;
【基金】：河南省高等学校重点科研基金资助项目(16A520066;17A520052)
【分类号】：TN713
本文编号：2257461
[Abstract]:In order to solve the problem that it is difficult for the probability assumption density filter to estimate the number of targets and the state of the target under low detection probability, a new algorithm of Gao Si hybrid probability assumption density filter based on multi-frame fusion is proposed. According to the target weights at different times, the target multi-frame weight record set and the target state extraction mark are constructed. When the target is missed at some time, the state of the lost target is estimated by extracting the target state and combining the weight information of the target multiple frame weight record set. Simulation results show that the algorithm can effectively improve the target state and number estimation accuracy of the existing algorithms under low detection probability.
【作者单位】： 商丘职业技术学院;
【基金】：河南省高等学校重点科研基金资助项目(16A520066;17A520052)
【分类号】：TN713
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