Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박치현 | - |
dc.date.accessioned | 2022-05-17T02:17:36Z | - |
dc.date.available | 2022-05-17T02:17:36Z | - |
dc.date.issued | 2020-09 | - |
dc.identifier.citation | IEEE Transactions on Wireless Communications, v. 19, no. 9, page. 5819-5832 | en_US |
dc.identifier.issn | 1536-1276 | - |
dc.identifier.issn | 1558-2248 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9107506 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/170910 | - |
dc.description.abstract | This paper presents robust localization algorithms that use range measurements to estimate the location parameters. The non-line-of-sight (NLOS) propagation of a signal can severely deteriorate the estimation performance in indoor and population-dense urban areas. Therefore, the robust localization algorithms are considered in this paper. In particular, the robust statistics-based localization is dealt with. The maximum likelihood (ML)-type and multi-stage ML-type method-based weighted least squares (WLS) algorithms and robust extrapolated single propagation unscented Kalman filter (ESPUKF) are proposed for mixed line-of-sight (LOS)/NLOS environments. Based on extensive simulations, the positioning accuracies of the proposed methods are found to be superior to those of conventional methods in the mildly and moderately mixed LOS/NLOS conditions. In addition, analyses are conducted on the mean square error (MSE), asymptotical unbiasedness and computational complexity of the proposed algorithms. | en_US |
dc.description.sponsorship | This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant funded by the Korean Government (MSIT), Intelligent Signal Processing for AI Speaker Voice Guardian, under Grant 2017-0-00474 and in part by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) under Grant NRF-2017R1D1A1B03032895. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.subject | Localization | en_US |
dc.subject | Robust | en_US |
dc.subject | maximum likelihoodtype estimator (M estimator) | en_US |
dc.subject | multi-stage maximum likelihoodtype estimator (MM estimator) | en_US |
dc.subject | extrapolated single propagation unscented Kalman filter | en_US |
dc.subject | weighted least squares | en_US |
dc.title | Robust Localization Based on ML-Type, Multi-Stage ML-Type, and Extrapolated Single Propagation UKF Methods under Mixed LOS/NLOS Conditions | en_US |
dc.type | Article | en_US |
dc.relation.no | 9 | - |
dc.relation.volume | 19 | - |
dc.identifier.doi | 10.1109/TWC.2020.2997455 | - |
dc.relation.page | 5819-5832 | - |
dc.relation.journal | IEEE Transactions on Wireless Communications | - |
dc.contributor.googleauthor | Park, Chee-Hyun | - |
dc.contributor.googleauthor | Chang, Joon-Hyuk | - |
dc.relation.code | 2020009612 | - |
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 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.