Cooperative shift estimation of target trajectory using clustered sensors
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In this paper, a mathematical model for target tracking using nonlinear scalar range sensors is formulated first. A time-shift sensor scheduling strategy is addressed on the basis of a k-barrier coverage protocol and all the sensors are divided into two classes of clusters, active cluster, and submissive cluster, for energy-saving. Then two types of time-shift nonlinear filters are proposed for both active and submissive clusters to estimate the trajectory of the moving target with disturbed dynamics. The stochastic stability of the two filters is analyzed. Finally, some numerical simulations are given to demonstrate the effectiveness of the new filters with a comparison of EKF.
Key wordsCluster sensor network target tracking time-shift estimation
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- Jazwinski A H, Stochastic Process and Filtering Theory, Dover Publications, 2007.Google Scholar
- Julier S, Uhlmann J, and Durrant-Whyte H, A new approach for filtering nonlinear system, Proc. of the American Control Conference, Washington, DC, 1995, 1628–1632.Google Scholar
- He Y and Chong E, Sensor scheduling for target tracking in sensor networks, Proc. of the 43rd IEEE CDC, Atlantis, Bahamas, 2004, 743–748.Google Scholar
- Kumar S, Lai T H, Posner M E, and Sinha P, Optimal sleep-wakeup algorithms for barriers of wireless wensors, Proc. of the 4th International Conference on Broadband Communications, Networks and Systems (BROADNETS), 2007, 327–336.Google Scholar
- Kumar S, Lai T H, and Arora A, Barrier coverage with wireless sensors, Proc. of the 11th Annual International Conference on Mobile Computing and Networking (ACM MobiCom), 2005, 284–298.Google Scholar
- Zhang H and Hou J C, Is deterministic deployment worse than random deployment for wireless sensor networks?, Proc. of the 25th IEEE International Conference on Computer Communications (INFOCOM), Spain, 2006, 1–13.Google Scholar
- Cheng D, Ghosh B, and Hu X, Distributed sensor network for target tracking, Proc. of MTNS 2006, Kyoto, 2006, 2171–2183.Google Scholar