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Journal Article

A Deep Learning Method for NLOS Error Mitigation in Coastal Scenes

Chao Sun; Meiting Xue; Nailiang Zhao; Yan Zeng; Junfeng Yuan; Jilin Zhang
Journal of Marine Science and Engineering · Vol. 10, Issue 12 · pp. 1952 · 2022

Abstract

With the widespread use of automatic identification systems (AISs), some ships use deceptive information or intentionally close their AISs to conceal their illegal activities or evade the supervision of maritime departments. Although radar measurements can be effectively utilized to evaluate the credibility of received AIS data, the propagation of non-line-of-sight (NLOS) signal conditions is an important factor that affects location accuracy. This study addresses the NLOS problem in a special geometric dilution of precision (GDOP) scenario on a coast and several base stations. We employed data augmentation and a deep residual shrinkage network in order to alleviate the adverse effects of NLOS errors. The results of our simulations demonstrate that the proposed method outperforms other range-based localization algorithms in a mixed LOS/NLOS environment. For a special GDOP scenario with four radars, our algorithm’s root-mean-square error (RMSE) was lower than 180 m.

Bibliographic Information

JournalJournal of Marine Science and Engineering
PublisherMDPI
Publication Date2022-12-08
Publication Year2022
Volume10
Issue12
Pages1952
Document TypeJournal Article
eISSN2077-1312
DOI10.3390/jmse10121952
SubjectMarine science; oceanography; marine engineering; coastal science; marine environment

Access Information

NARA Access CoverageOA / free full text
Journal Homepagehttps://www.mdpi.com/journal/jmse
Publisher PageOpen Publisher Page
This article is openly available from the publisher.