Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming

A new cooperative received signal strength-based localization algorithm is proposed which employs relative error estimation and semidefinite programming (SDP). First, the log-normal shadowing RSS measurement model is transformed into an equivalent multiplicative model. Then, a relative error estimat...

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Vydáno v:IEEE transactions on vehicular technology Ročník 68; číslo 1; s. 483 - 497
Hlavní autoři: Wang, Zengfeng, Zhang, Hao, Lu, Tingting, Gulliver, T. Aaron
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Convex functions
Cooperative localization
Error analysis
Error detection
Localization
Lower bounds
maximum likelihood (ML) estimator
Maximum likelihood estimation
Maximum likelihood estimators
Measurement uncertainty
Random variables
received signal strength (RSS)
relative error estimation
Remote sensors
Semidefinite programming
semidefinite programming (SDP)
Shadow mapping
Signal strength
Wireless sensor networks
ISSN:0018-9545, 1939-9359
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Shrnutí:A new cooperative received signal strength-based localization algorithm is proposed which employs relative error estimation and semidefinite programming (SDP). First, the log-normal shadowing RSS measurement model is transformed into an equivalent multiplicative model. Then, a relative error estimation criterion is used with this model to develop a nonconvex estimator to approximate the maximum likelihood solution. Finally, semidefinite relaxation is applied to the nonconvex estimator to obtain an SDP estimator. The proposed algorithm is first derived for noncooperative RSS-based localization and then extended to the cooperative case. The Cramer-Rao lower bound is derived for cooperative RSS-based localization. Performance results are presented, which demonstrate that the proposed SDP estimator provides a significant improvement over existing localization methods.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2018.2880991