| 引用本文: | 徐洋,徐晖,罗少华,等.基于随机有限集理论的多传感器目标联合检测跟踪算法.[J].国防科技大学学报,2013,35(1):89-96.[点击复制] |
| XU Yang,XU Hui,LUO Shaohua,et al.Multisensor joint target detection and tracking algorithm based on random finite sets[J].Journal of National University of Defense Technology,2013,35(1):89-96[点击复制] |
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| 基于随机有限集理论的多传感器目标联合检测跟踪算法 |
| 徐洋1,2, 徐晖3, 罗少华3, 安玮3 |
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(1.空军装备研究院 雷达与电子对抗研究所，北京 100085;2.
2.国防科技大学 电子科学与工程学院，湖南 长沙 410073;3.2.国防科技大学 电子科学与工程学院，湖南 长沙 410073)
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| 摘要: |
| 针对杂波环境中的目标检测跟踪问题，提出一种基于随机有限集理论的多传感器目标联合检测跟踪算法。算法将目标状态和量测描述为随机集合，建立考虑目标出现、目标保持、目标消失等情况的目标状态随机有限集模型，以及考虑漏检和虚警的多传感器量测随机有限集模型。将目标的联合检测跟踪问题构建为目标状态集合的贝叶斯最优估计问题，并基于随机有限集理论对该贝叶斯估计算法的递推表达式进行严格理论推导。采用序贯蒙特卡罗技术实现算法的递推滤波。仿真结果验证了该算法的有效性以及算法相对于传统基于数据关联算法的性能优势。 |
| 关键词: 多传感器 联合检测跟踪 随机有限集 序贯蒙特卡洛 贝叶斯方法 |
| DOI： |
| 投稿日期：2012-05-26 |
| 基金项目:国家部委资助项目 |
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| Multisensor joint target detection and tracking algorithm based on random finite sets |
| XU Yang1,2, XU Hui3, LUO Shaohua3, AN Wei3 |
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(1.Radar and Electric Countermeasure Research Institute, Air Force Equipment Academy, Beijing 100085, China;2.
2.College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;3.2.College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| A joint detection and tracking algorithm based on Random Finite Sets (RFS) theory was proposed for target detection and tracking in presence of clutters using multiple sensors. First, target states and measurements were described as RFS variables, RFS models of target motion including target birth, target survival and target death, and the multisensor measurements including miss detection and false alarm were constructed. The joint target detection and tracking problem was then modeled as a Bayesian optimal estimation to the target state RFS and the theoretically rigorous recursive formulas for the estimation were derived by using RFS theory. Finally, Sequential Monte Carlo (SMC) implementation was presented to the filter recursion. Simulation results demonstrate the effectiveness of the proposed algorithm and its significant improvement in performance over traditional association-based ones. |
| Keywords: multisensor joint detection and tracking random finite set Sequential Monte Carlo Bayesian method |
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