| 传感器网络下机动目标动态协同跟踪算法 |
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| 引用本文： | 杨小军,邢科义,施坤林,潘泉.传感器网络下机动目标动态协同跟踪算法[J].自动化学报,2007,33(10):1029-1035. |
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| 作者姓名： | 杨小军 邢科义 施坤林 潘泉 |
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| 作者单位： | 1.西安交通大学系统工程研究所机械制造系统工程国家重点实验室 西安 710049 |
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| 基金项目： | 国家自然科学基金;中国博士后科学基金 |
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| 摘 要： | 对传感器网络下的机动目标跟踪问题提出一种分布式传感器节点动态分簇、协同跟踪算法. 通过在线优化目标跟踪的性能函数和通讯代价, 自适应地选择节点并动态分簇, 通过多传感器节点的协同感知以及信息融合提高了跟踪精度. 由于问题的非线性和传感器节点的随机性, 本文基于粒子滤波器在线预测和估计目标状态的概率分布, 使用混合高斯粒子滤波器以及选择最短路径用于传感器节点之间的信息交换节约了通讯能量, 通过一种有效的粒子方法逼近目标状态的预测方差以实现传感器节点的最优选择. 仿真结果表明, 与 IDSQ 算法相比较, 本文提出的动态分簇算法实现了对机动目标的高精度跟踪.
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| 关 键 词： | 传感器网络 传感器协同 Bayes推理 粒子滤波 |
| 收稿时间： | 2006-7-26 |
| 修稿时间： | 2006-07-26 |
Dynamic Collaborative Algorithm for Maneuvering Target Tracking in Sensor Networks |
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| YANG Xiao-Jun,XING Ke-Yi,SHI Kun-Lin,PAN Quan.Dynamic Collaborative Algorithm for Maneuvering Target Tracking in Sensor Networks[J].Acta Automatica Sinica,2007,33(10):1029-1035. |
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| Authors: | YANG Xiao-Jun XING Ke-Yi SHI Kun-Lin PAN Quan |
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| Affiliation: | 1.The State Key Laboratory for Manufacturing System Engineering, System Engineering Institute, Xi'an Jiaotong University, Xi'an 710049;2.National Key Laboratory of Electromechanical Engineering and Control, Xi'an Institute of Electromechanical Information Technology, Xi'an 710065;3.School of Automaton, Northwestern Polytechnical University, Xi'an 710072 |
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| Abstract: | A distributed dynamic clustering and collaborative tracking algorithm is proposed for maneuvering target tracking problems in sensor networks.The sensor node is selected adaptively and a sensor cluster is activated online by optimizing the performance measure of tracking and cost of communication.Accuracy of tracking is improved by dynamic collaboration and information fusion of the sensor nodes.The particle filtering is employed to predict and estimate the probability distribution of target states due to nonlinear problems and randomness of the sensor nodes.The Gaussian mixture particle filtering and the shortest routing algorithm are utilized for information exchange between the sensor nodes to save energy of communication.An efficient particle method is proposed for approximating expected posterior mean square error to optimize sensor selection.The simulation shows significant improvement of the proposed algorithm over existing IDSQ methods in tracking accuracy for maneuvering target. |
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| Keywords: | Sensor network sensor collaboration Bayesian inference particle filtering |
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