Semidefinite Programming for Wireless Cooperative Localization Using Biased RSS Measurements

Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are gene...

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Veröffentlicht in:IEEE communications letters Jg. 26; H. 6; S. 1278 - 1282
Hauptverfasser: Wang, Qi, Duan, Zhansheng, Li, Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bias
Convexity
cooperative localization
Localization
Location awareness
Maximum likelihood estimation
Maximum likelihood estimators
Measurement uncertainty
Nodes
Noise measurement
Norms
observation bias
Optimization
RSS
SDP
Semidefinite programming
Signal strength
Time measurement
Wireless communication
Wireless sensor networks
ISSN:1089-7798, 1558-2558
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Zusammenfassung:Cooperative localization in wireless sensor network (WSN) using biased received signal strength (RSS) measurements is investigated in this letter. In the existing work on cooperative RSS localization, measurements of sensor nodes (including both target-anchor and target-target measurements) are generally assumed bias-free. However, in practice, they may be subject to biases, which directly affect localization accuracy. As a result, the existing localization methods are not applicable any more. In this letter, RSS observation biases are considered as the extra parameters to be estimated as well as locations of target nodes. To overcome the nonconvexity of the maximum likelihood (ML) estimator, semidefinite programming (SDP) is applied with <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">l_{2} </tex-math></inline-formula> norms, respectively. Then, the locations of multiple target nodes and observation biases are simultaneously estimated through convex optimization. Numerical examples demonstrate the performance superiority of the proposed methods compared to the existing bias-free SDP methods for wireless cooperative localization.
Bibliographie:ObjectType-Article-1
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3166780