3-D Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements

This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., PT . We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 66; H. 4; S. 3197 - 3210
Hauptverfasser: Tomic, Slavisa, Beko, Marko, Dinis, Rui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Angle-of-arrival (AoA)
Computer simulation
Economic models
Fuses
generalized trust region subproblem (GTRS)
Hybrid systems
Linear programming
Localization
Maximum likelihood estimation
Maximum likelihood estimators
Programming
Propagation losses
Rangefinding
received signal strength (RSS)
Remote sensors
Robot sensing systems
Semidefinite programming
semidefinite programming (SDP)
Sensors
Signal strength
wireless localization
wireless sensor network (WSN)
Wireless sensor networks
ISSN:0018-9545, 1939-9359
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Zusammenfassung:This paper addresses target localization problems in both noncooperative and cooperative 3-D wireless sensor networks (WSNs), for both cases of known and unknown sensor transmit power, i.e., PT . We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength and angle-of-arrival information, respectively. Based on range and angle measurement models, we derive a novel nonconvex estimator based on the least squares criterion. The derived nonconvex estimator tightly approximates the maximum-likelihood estimator for small noise. We then show that the developed estimator can be transformed into a generalized trust region subproblem framework, by following the squared range approach, for noncooperative WSNs. For cooperative WSNs, we show that the estimator can be transformed into a convex problem by applying appropriate semidefinite programming relaxation techniques. Moreover, we show that the generalization of the proposed estimators for known PT is straightforward to the case where PT is not known. Our simulation results show that the new estimators have excellent performance and are robust to not knowing PT . The new estimators for noncooperative localization significantly outperform the existing estimators, and our estimators for cooperative localization show exceptional performance in all considered settings.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2016.2589923