二进制传感器网络加权目标跟踪算法研究

孙晓艳; 李建东; 陈彦辉; 张文柱; 姚俊良

doi:10.3724/SP.J.1146.2009.01640

二进制传感器网络加权目标跟踪算法研究

doi: 10.3724/SP.J.1146.2009.01640

孙晓艳^{①② 李建东,},
李建东,
陈彦辉,
张文柱,
姚俊良

基金项目:

国家杰出青年科学基金(60725105)，国家973计划项目(2009CB320404)，长江学者和创新团队发展计划(IRT0852)，国家自然科学基金(60902032)，国家重点实验室专项基金(ISN02080001)，高等学校创新引智计划(B08038)和教育部科学技术研究重点项目(107103)资助课题

计量
- 文章访问数: 3778
- HTML全文浏览量: 92
- PDF下载量: 819
- 被引次数: 0
出版历程
- 收稿日期: 2009-12-29
- 修回日期: 2010-06-03
- 刊出日期: 2010-09-19

The Study on Weighted Target Tracking Algorithm for Binary Sensor Networks

摘要

摘要: 该文主要研究二进制传感器网络中加权目标跟踪算法的设计。针对已有算法中权值不能实时反映目标与感测节点之间距离关系的缺点，提出了距离加权和基于预测的距离加权目标跟踪算法。距离权值能够实时反映目标与各个感测节点间的距离关系，因此具有更高的跟踪精度。在距离加权算法中感测节点需要将感测信息和距离信息都传输到融合中心，这会增大感测节点的能量消耗。为了解决这个问题，文中提出一种基于预测的距离加权目标跟踪算法。该算法中感测节点不需要传输距离信息而只传输感测信息到融合中心从而减少了能耗。仿真结果表明，基于预测的距离加权算法比已有算法能够够精确地跟踪目标，在保证跟踪精度的同时减少了通信能耗。
- 二进制传感器网络 /
- 目标跟踪 /
- 距离加权 /
- 预测距离加权
Abstract: The research focus of this paper is to design a novel target tracking algorithm for binary sensor network (BSN). The weight in existing algorithms can not reflect the realtime relationship between target and sensing nodes. Distance weighted target tracking algorithm is proposed, in which the distance weight can reflect the realtime relationship between target and senisng nodes and thererfore has better performance than the existing algorithms. In distance weighted algorithm, sensing node should transmit both sensing information and distance information to the fusion center and consum more energy. Prediction-based distance weighted target tracking algorithm is provided, which can reduce energy consumption by the transmission of only sensing information. Simulation results show that prediction-based distance weighted algorithm can not only guarantee the tracking accuracy but also reduce energy consumption, compared with the existent algorithms.
- Binary Sensor Network (BSN) /
- Target tracking /
- Distance Weight (DW) /
- Prediction-based Distance Weight (PDW)

HTML全文

参考文献(1)

[1] Akyildiz I, Sun W, and Sankarasubramaniam Y, et al.. A survey on sensor networks. IEEE Communications Magazine, 2002, 40(8): 102-114. [2] 陈积明，张艳平，曹向辉等. 基于声强的无线传感器网络目标跟踪方法研究[J].电子与信息学报.2009, 31(11):2791-2794浏览 Chen Ji-ming, Zhang Yan-ping, and Cao Xiang-hui, et al.. Acoustic energy based scheme for target tracking in wireless sensor networks[J].Journal of Electronics Information Technology.2009, 31(11):2791-2794 [3] Mechitov Kirill, Sundresh Sameer, and Kwon Youngmin, et al.. Cooperative tracking with binary-detection sensor networks[R]. Technical Report UIUCDCS-R-2003-279, Department of Computer Science, University of Illinois at Urbana-Champaign, 2003. [4] 李煜，程远国，杨露菁. 二元探测传感器网络的递推加权质心目标定位算法[J]. 海军工程大学学报，2008, 20(5): 42-45. Li Yu, Cheng Yuan-guo, and Yang Lu-jing. A resursive weighted centroid algorithm for object location in binary sensor networks[J]. Journal of Naval University of Engineering, 2008, 20(5): 42-45. [5] Djuric P M.[J].Vemula M, and Bugallo M F. Signal processing by particle filtering for binary sensor networks. Proc. IEEE 11th Digital Signal Processing Workshop IEEE Signal Processing Education Workshop, Millenia Singapore.2004,:- [6] Vemula M, Bugallo M F, and Djuric P M. Particle filtering-based target tracking in binary sensor networks using adaptive thresholds. Proc. The 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Virgin Islands, American, 2007: 17-20. [7] Djuric P M, Vemula M, and Bugallo M F. Target tracking by particle filtering in binary sesnsor networks[J].IEEE Transactions on Signal Processing.2008, 56(6):2229-2238 [8] Raghunathan V, Schurgers C, and Partk S, et al.. Energy-aware wireless microsensor networks[J].IEEE Signal Processing Magazine.2002, 19(2):40-50 [9] 赫金(Haykin.S.)著，郑宝玉等译. 自适应滤波器原理(第4版)[M]. 北京：电子工业出版社，2006-11-1, 344-366.

施引文献

资源附件(0)

访问统计