Decentralized positioning and tracking based on a variable step-size incremental subgradient algorithm for wireless sensor networks

In this paper, we propose a modified incremental subgradient (MIG) algorithm with a variable step size for positioning and tracking a target in wireless sensor networks. The proposed positioning scheme formulates location estimation as a nonlinear least-squares problem using the received signal strength, and then applies the MIG algorithm with a fixed step size to solve the problem. This scheme can be realized in an iterative, decentralized manner to improve both bandwidth and energy efficiencies. To track a moving target, we further present a step-size adjustment mechanism based on the velocity of the target. In addition, a convergence analysis is given for the MIG-based positioning process. As compared with related positioning and tracking methods, the proposed scheme has better location accuracy and tracking performance.

[1]  Michael Rabbat,et al.  Decentralized source localization and tracking , 2004 .

[2]  Robert D. Nowak,et al.  Decentralized source localization and tracking [wireless sensor networks] , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  D. Bertsekas,et al.  Convergen e Rate of In remental Subgradient Algorithms , 2000 .

[4]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[5]  Chin-Liang Wang,et al.  An Adaptive Location Estimator Based on Alpha-Beta Filtering for Wireless Sensor Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[6]  Chin-Liang Wang,et al.  An Adaptive Location Estimator Based on Kalman Filtering for Wireless Sensor Networks , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[7]  Chin-Liang Wang,et al.  A Decentralized Positioning Method for Wireless Sensor Networks Based on Weighted Interpolation , 2007, 2007 IEEE International Conference on Communications.

[8]  Prashant Krishnamurthy,et al.  Modeling of indoor positioning systems based on location fingerprinting , 2004, IEEE INFOCOM 2004.

[9]  Supachai Phaiboon,et al.  An empirically based path loss model for indoor wireless channels in laboratory building , 2002, 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM '02. Proceedings..