Data networks
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
The cricket compass for context-aware mobile applications
Proceedings of the 7th annual international conference on Mobile computing and networking
Proceedings of the 5th international conference on Information processing in sensor networks
PhotoBeacon: design of an optical system for localization and communication in multi-robot systems
Proceedings of the 1st international conference on Robot communication and coordination
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
RF angle of arrival-based node localisation
International Journal of Sensor Networks
Radio interferometric angle of arrival estimation
EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
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Sensor location information is a prerequisite to the utility of most sensor networks. In this paper we present a robust and low-complexity algorithm to self-localize and orient sensors in a network based on angle-of-arrival (AOA) information. The proposed non-iterative subspace-based method is robust to missing and noisy measurements and works for cases when sensor orientations are either known or unknown. We show that the computational complexity of the algorithm is O (mn2), where m is the number of measurements and n is the total number of sensors. Simulation results demonstrate that the error of the proposed subspace algorithm is only marginally greater than an iterative maximum-likelihood estimator (MLE), while the computational complexity is two orders of magnitude less. Additionally, the iterative MLE is prone to converge to local maxima in the likelihood function without accurate initialization. We illustrate that the proposed subspace method can be used to initialize the MLE and obtain near-Cramér-Rao performance for sensor localization. Finally, the scalability of the subspace algorithm is illustrated by demonstrating how clusters within a large network may be individually localized and then merged.