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A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks
Shanghai Maritime Univ, Peoples Republic of China.ORCID iD: 0000-0002-1831-4329
Shanghai Maritime Univ, Peoples Republic of China.ORCID iD: 0000-0002-3150-3407
Shanghai Maritime Univ, Peoples Republic of China.
Linnaeus University, Faculty of Technology, Department of Informatics. Shanghai Maritime Univ, Peoples Republic of China.
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2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 12, p. 1-18, article id 2708Article in journal (Refereed) Published
Abstract [en]

As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect-signal shadowing by waves and unintentional or malicious attacks-outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer-Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 19, no 12, p. 1-18, article id 2708
Keywords [en]
bisection method, ocean sensor networks, outlier measurements, non-cooperative localization, received signal strength indication
National Category
Information Systems
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
URN: urn:nbn:se:lnu:diva-86960DOI: 10.3390/s19122708ISI: 000473762500060PubMedID: 31208142Scopus ID: 2-s2.0-85068375227OAI: oai:DiVA.org:lnu-86960DiVA, id: diva2:1338627
Available from: 2019-07-23 Created: 2019-07-23 Last updated: 2019-08-29Bibliographically approved

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