Cooperative Localization in Wireless Sensor Networks With AOA Measurements

This paper researches the cooperative localization in wireless sensor networks (WSNs) with <inline-formula> <tex-math notation="LaTeX">2\pi /\pi </tex-math></inline-formula>-periodic angle-of-arrival (AOA) measurements. Two types of localizers are developed from the...

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Vydáno v:IEEE transactions on wireless communications Ročník 21; číslo 8; s. 6760 - 6773
Hlavní autoři: Wang, Shengchu, Jiang, Xianbo, Wymeersch, Henk
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Antenna arrays
Antenna measurements
Approximate message passing
Bayesian analysis
Computational geometry
Convex analysis
Convexity
cooperative localization
expectation maximization
Importance sampling
least square
Localization
Location awareness
Lower bounds
Maximum likelihood estimation
Maximum likelihood estimators
Message passing
Optimization
Orientation
Pollution measurement
Quantization (signal)
Statistical inference
Wireless networks
Wireless sensor networks
ISSN:1536-1276, 1558-2248, 1558-2248
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Shrnutí:This paper researches the cooperative localization in wireless sensor networks (WSNs) with <inline-formula> <tex-math notation="LaTeX">2\pi /\pi </tex-math></inline-formula>-periodic angle-of-arrival (AOA) measurements. Two types of localizers are developed from the perspectives of Bayesian inference and convex optimization. When the orientation angles are known, the positioning problem is resolved by a phase-only generalized approximate message passing (POG-AMP) algorithm with importance sampling mechanism. From the perspective of convex optimization, the positioning problem under <inline-formula> <tex-math notation="LaTeX">2\pi /\pi </tex-math></inline-formula>-periodic AOAs is converted as a least square (LS) problem and then resolved by the gradient-descent/projected gradient-descent method named as Type-I LS localizer. When the orientations are unknown, expectation-maximization (EM) mechanism is introduced into the POG-AMP localizer, where node positions and orientations are alternatively updated through exchanging their statistical confidences. Type-II LS localizer is constructed by alternatively executing Type-I LS and a maximum-likelihood (ML) estimator of orientation. Cramér-Rao lower bounds (CRLBs) are derived for the proposed localizers. Simulation results validate that the proposed AMP-type and LS-type localizers outperform existing localizers, AMP-type localizers successfully handle nonlinear quantization losses, and EM-framework and ML estimator handle unknown orientation problem. AMP-type localizers outperform LS-type ones, and can approach to the CRLBs even under high noise contaminations.
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ISSN:1536-1276
1558-2248
1558-2248
DOI:10.1109/TWC.2022.3152426