RSS-based sensor localization with unknown transmit power

Received signal strength (RSS)-based single source localization when there is not a prior knowledge about the transmit power of the source is investigated. Because of nonconvex behavior of maximum likelihood (ML) estimator, convoluted computations are required to achieve its global minimum. Therefore, we propose a novel semidefinite programming (SDP) approach by approximating ML problem to a convex optimization problem which can be solved very efficiently. Computer simulations show that our proposed SDP has a remarkable performance very close to ML estimator. Linearizing RSS model, we also derive the partly novel least squares (LS) and weighted total least squares (WTLS) algorithms for this problem. Simulations illustrate that WTLS improves the performance of LS considerably.

[1]  Wing-Kin Ma,et al.  A Constrained Least Squares Approach to Mobile Positioning: Algorithms and Optimality , 2006, EURASIP J. Adv. Signal Process..

[2]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[3]  Chin-Tau A. Lea,et al.  Received Signal Strength-Based Wireless Localization via Semidefinite Programming: Noncooperative and Cooperative Schemes , 2010, IEEE Transactions on Vehicular Technology.

[4]  Reza M. Vaghefi,et al.  Bearing-only target localization with uncertainties in observer position , 2010, 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops.

[5]  Sabine Van Huffel,et al.  The element-wise weighted total least-squares problem , 2006, Comput. Stat. Data Anal..

[6]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[7]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[8]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[9]  Gang Wang,et al.  Efficient semidefinite relaxation for energy-based source localization in sensor networks , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Zhi Ding,et al.  A Semidefinite Programming Approach to Source Localization in Wireless Sensor Networks , 2008, IEEE Signal Processing Letters.

[11]  Sabine Van Huffel,et al.  Overview of total least-squares methods , 2007, Signal Process..

[12]  Alfred O. Hero,et al.  Relative location estimation in wireless sensor networks , 2003, IEEE Trans. Signal Process..