Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박치현 | - |
dc.date.accessioned | 2022-09-22T01:50:49Z | - |
dc.date.available | 2022-09-22T01:50:49Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.citation | IEEE Access, v. 9, page. 4059-4071 | en_US |
dc.identifier.issn | 2169-3536 | en_US |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9311118 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/173183 | - |
dc.description.abstract | Robust localization methods that employ distance measurements to predict the position of an emitter are proposed in this paper. The occurrence of outliers due to the non-line-of sight (NLOS) propagation of signals can drastically degrade the localization performance in crowded urban areas and indoor situations. Hence, robust positioning methods are considered to mitigate the effects of outliers. Specifically, localization methods based on robust statistics are considered. Modified multi-stage ML-type method (MM) based weighted least squares (WLS), maximum a posteriori (MAP) expectation maximization (EM) WLS and variational Bayes (VB) EM WLS algorithms are developed under various outlier-contaminated environments. Simulation results show that the position estimation accuracy of the proposed modified MM WLS method, which uses the novel weight, is higher than that of the other methods under most outlier-contaminated conditions. Furthermore, the MAP-EM WLS and VB-EM WLS methods are the most accurate among algorithms that do not require statistical testing. Additionally, the mean square error (MSE) and asymptotic unbiasedness of the proposed algorithms are analyzed. | en_US |
dc.description.sponsorship | This work was supported in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), Korean Government under Grant 2020R1A2C1011503, and in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) funded by the Korean Government (MSIT) through the Artificial Intelligence Graduate School Program (Hanyang University) under Grant 2020-0-01373. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.subject | Expectation maximization; localization; maximum a posteriori; multi-stage maximum likelihood-type (MM) estimator; robust; variational Bayes; weighted least squares | en_US |
dc.title | Modified MM Algorithm and Bayesian Expectation Maximization-based Robust Localization under NLOS Contaminated Environments | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ACCESS.2020.3048154 | en_US |
dc.relation.page | 4059-4071 | - |
dc.relation.journal | IEEE Access | - |
dc.contributor.googleauthor | Park, Chee-Hyun | - |
dc.contributor.googleauthor | Chang, Joon-Hyuk | - |
dc.relation.code | 2020009448 | - |
dc.sector.campus | S | - |
dc.sector.daehak | RESEARCH INSTITUTE[S] | - |
dc.sector.department | RESEARCH INSTITUTE OF ELECTRICAL & COMPUTER ENGINEERING | - |
dc.identifier.pid | cheehyunp | - |
dc.identifier.orcid | https://orcid.org/0000-0002-9739-5277 | - |
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