Optimal and Adaptive Signal Processing
Optimal and Adaptive Signal Processing
Incremental Least Squares Methods and the Extended Kalman Filter
SIAM Journal on Optimization
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Real-Time Mobility Tracking Algorithms for Cellular Networks Based on Kalman Filtering
IEEE Transactions on Mobile Computing
Wireless sensor network localization techniques
Computer Networks: The International Journal of Computer and Telecommunications Networking
The NTP experimental platform for heterogeneous wireless sensor networks
Proceedings of the 4th International Conference on Testbeds and research infrastructures for the development of networks & communities
Computer
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Energy-based sensor network source localization via projection onto convex sets
IEEE Transactions on Signal Processing
Estimating signal strengths in the design of an indoor wireless network
IEEE Transactions on Wireless Communications
IEEE Communications Magazine
Quantized incremental algorithms for distributed optimization
IEEE Journal on Selected Areas in Communications
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This paper presents a decentralized positioning and tracking method based on recursive weighted least-squares optimization for wireless sensor networks. The proposed algorithm--weighted extended Kalman filter--is derived by minimizing a recursive-in-time objective function and then applying it in an iterative decentralized manner. The target location is calculated iteratively by taking a weighted average of the local estimates based on the participating sensor nodes' reliability, where a participating sensor node computes the newest location estimate according to its own observation and the most recent local estimate passed from the previous participating sensor node. A convergence analysis is given to show the convergence behavior of the proposed algorithm. To track the target in the network, a message-passing algorithm is proposed for adaptively selecting the participating sensor nodes as the target moves around the area. During each iteration, the current participating sensor node computes the local estimate and passes it on to the next participating sensor node for further processing. The update process is circulated only among the selected participating sensor nodes that surround the target. Computer simulation results show that our proposed algorithm outperforms previous related methods.