Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Ranging in a dense multipath environment using an UWB radio link
IEEE Journal on Selected Areas in Communications
EURASIP Journal on Advances in Signal Processing
Range-based localization for UWB sensor networks in realistic environments
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
A novel non-iterative localization solution
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
An optimization approach to single-source localization using direction and range estimates
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A framework for low complexity least-squares localization with high accuracy
IEEE Transactions on Signal Processing
Fundamental limits and improved algorithms for linear least-squares wireless position estimation
Wireless Communications & Mobile Computing
Wireless Personal Communications: An International Journal
An efficient non-line-of-sight error mitigation method for TOA measurement in indoor environments
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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In this paper, we propose a linear programming approach to the problem of non-line-of-sight (NLOS) error mitigation in sensor networks. The locations of sensor nodes can be estimated using range or distance estimates from location-aware "anchor" nodes. In the absence of line-of-sight (LOS) between the sensor and anchor nodes, e.g., in indoor networks, the NLOS range estimates can be severely biased. If these biased range estimates are directly incorporated into practical location estimators such as the Least-Squares (LS) estimator without the mitigation of these bias errors, this can potentially lead to degradation in the accuracy of sensor location estimates. On the other hand, discarding the biased range estimates may not be a viable option, since the number of range estimates available may be limited. We present a novel NLOS bias mitigation scheme, based on linear programming, that (i) allows us to incorporate NLOS range information into sensor location-estimation, but (ii) does not allow NLOS bias errors to degrade sensor localization accuracy.