基于概率假设密度的近邻目标快速跟踪算法
TP391; 针对杂波场景中多个目标相互近邻时,标准概率假设密度滤波器难以正确估计目标状态且计算复杂度高等问题,提出一种基于概率假设密度的近邻目标快速跟踪算法.所提算法首先采用自适应门技术从传感器观测集中划分出源于真实目标的观测集,随后利用真实目标观测集来更新预测强度,最后应用检测导向的近邻目标权重校正方法有选择地重分配各离散时刻后验强度中不精确的分量权重.实验结果表明,所提算法不仅具有高效率的目标跟踪,而且具有良好的鲁棒性....
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| Published in | 计算机应用研究 Vol. 38; no. 4; pp. 1220 - 1223 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
商丘职业技术学院 机电系,河南 商丘476000
2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.19734/j.issn.1001-3695.2020.03.0049 |
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| Abstract | TP391; 针对杂波场景中多个目标相互近邻时,标准概率假设密度滤波器难以正确估计目标状态且计算复杂度高等问题,提出一种基于概率假设密度的近邻目标快速跟踪算法.所提算法首先采用自适应门技术从传感器观测集中划分出源于真实目标的观测集,随后利用真实目标观测集来更新预测强度,最后应用检测导向的近邻目标权重校正方法有选择地重分配各离散时刻后验强度中不精确的分量权重.实验结果表明,所提算法不仅具有高效率的目标跟踪,而且具有良好的鲁棒性. |
|---|---|
| AbstractList | TP391; 针对杂波场景中多个目标相互近邻时,标准概率假设密度滤波器难以正确估计目标状态且计算复杂度高等问题,提出一种基于概率假设密度的近邻目标快速跟踪算法.所提算法首先采用自适应门技术从传感器观测集中划分出源于真实目标的观测集,随后利用真实目标观测集来更新预测强度,最后应用检测导向的近邻目标权重校正方法有选择地重分配各离散时刻后验强度中不精确的分量权重.实验结果表明,所提算法不仅具有高效率的目标跟踪,而且具有良好的鲁棒性. |
| Author | 王颖 |
| AuthorAffiliation | 商丘职业技术学院 机电系,河南 商丘476000 |
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| Author_FL | Wang Ying |
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| Author_xml | – sequence: 1 fullname: 王颖 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | 计算时间 权重再分配 状态估计 概率假设密度 近邻目标跟踪 |
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| Title | 基于概率假设密度的近邻目标快速跟踪算法 |
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