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Topology configuration of sensor networks based on distributed fusion |
GE Quan-bo1,2, LIU Shuang-jian1, WEN Cheng-lin1 |
1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; 2. Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract Dynamic network topology configuration (DNTC) was studied by adopting two optimal distributed asynchronous track fusion (ATF) methods for sensor networks with asynchronous sampling. Firstly, the local Kalman filtering is done in the network node and the estimates are transmitted to the processing center where different fusion operations are performed to realize predict calibration and recursive weighted fusion. Secondly, the realtime recursive fusion estimate is compared with the tracking precision and the decision to stop or continue is given. Finally, the DNTC and energy conservation can be realized. Moreover, the analysis of network energy conservation is also presented and the simulation examples are given to show the validity of the proposed methods. The results show that both the proposed methods can realize the DNTC function and energy conservation, and the method which adopts optimal fusion has better effect than the suboptimal one.
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Published: 14 July 2011
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基于分布式融合的传感器网络拓扑配置
采用2种最优分布式异步航迹融合方法研究异步采样传感器网络的动态拓扑配置策略设计.其主要核心思想是:每一个传感器跟踪节点执行局部Kalman滤波,再将滤波估计结果传输到融合中心;融合中心利用各自不同的融合方式执行预测估计校准和最优递推加权融合,同时利用当前所有传感器信息的全局递推融合估计与系统精度要求的阈值进行实时比较,以决定是否终止或继续进行融合;实现下一时刻网络拓扑的动态配置和网络节能.基于特定的计算准则分析网络能量消耗,并通过计算机仿真验证算法的有效性.结果显示:2种方法都能实现异步采样多传感器网络的动态拓扑配置和节能,且最优异步融合配置方法的效果优于次优异步航迹融合方法.
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[1] HOLGER K, ANDREAS W. Protocols and architectures for wireless sensor networks [M]. Beijing: Publishing House of Electronics Industry, 2007: 1-13. [2] SHI Ling. Resource optimization for networked estimator with guaranteed estimation quality [D]. Pasadena, California, USA: California Institute of Technology, 2009: 1-7. [3] SHI Ling, HARL H J, RICHARD M M. Change sensor topology when needed: how to efficiently use system resources in control and estimation over wireless networks[C]//Proceedings of the IEEE Conference on Decision and Control. New Orleans, USA: IEEE, 2007: 5478-5485. [4] 杨万海. 多传感器数据融合及其应用[M]. 西安: 西安电子科技大学出版社, 2004: 1-22. [5] BARSHALOM Y. On the track to track correlation problems [J]. IEEE Transactions on Automatic Control, 1981, 26(2): 571-572. [6] ALOUANI A T, GRAY J E, MCCABE D H. Theory of distributed estimation using multiple asynchronous sensors [J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(2): 717-722. [7] 金学波, 孙优贤. 相关测量噪声的多传感器最优融合状态估计[J]. 浙江大学学报: 工学版, 2003, 37(1): 60-64. JIN Xuebo, SUN Youxian. Optimal state estimation for data fusion with correlated measurement noise [J]. Journal of Zhejiang University: Engineering Science, 2003, 37(1): 60-64. [8] 余安喜, 胡卫东, 周文辉. 多传感器量测融合算法的性能比较[J]. 国防科技大学学报, 2003, 25(6): 39-44. YU Anxi, HU Weidong, ZHOU Wenhui. Performance comparison of multisensory measurement fusion algorithms [J]. Journal of National University of Defense Technology, 2003, 25(6): 39-44. [9] 文成林, 吕冰, 葛泉波. 一种基于分步式滤波的数据融合算法[J]. 电子学报, 2004, 32(8): 1264-1267. WEN Chenglin, LV Bing, GE Quanbo. A data fusion algorithm based on filtering step by step [J]. Acta Electronica Sinica, 2004, 32(8): 1264-1267. [10] WEN C L, GE Q B. Step by step prediction fusion based on asynchronous multisensory system [J]. Journal of Central South University, 2005, 32(S1): 652-653. [11] 文成林, 葛泉波, 刘双剑. 带有信息反馈的最优异步递推航迹融合算法[J]. 电子与信息学报, 2009, 31(9): 2123-2131. WEN Chenglin, GE Quanbo, LIU Shuangjian. Optimal asynchronous recursive track fusion with global feedback [J]. Journal of Electronics and Information Technology, 2009, 31 (9): 2123-2131. |
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