Robust Localization Method Based on Non-Parametric Probability Density Estimationopen access
- Authors
- Park, Chee-Hyun; Chang, Joon-Hyuk
- Issue Date
- Jun-2023
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Location awareness; Estimation; Testing; Probability density function; Maximum likelihood estimation; Computational modeling; Measurement uncertainty; First peak; Gaussian mixture model; k-nearest neighbor; localization; non-line-of-sight; orthogonal series; probability density function; weighted least squares
- Citation
- IEEE ACCESS, v.11, pp.61468 - 61480
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 11
- Start Page
- 61468
- End Page
- 61480
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188429
- DOI
- 10.1109/ACCESS.2023.3287140
- ISSN
- 2169-3536
- Abstract
- This paper presents robust localization techniques that calculate location using distance observations. In enclosed and heavily populated urban environments, the positive measurement bias introduced by a non-line-of-sight signal can have a considerable adverse impact on estimation performance. Therefore, to mitigate the detrimental effects of the multipath effect caused by the non-line-of-sight signal, robust localization techniques are considered. In particular, the ${k}$ -nearest neighbor (KNN)-based and orthogonal series (OSERIES)-based localization approaches are proposed. The difference from conventional probability density estimation (PDF) estimation methods is that the proposed methods use the first-peak information of the estimated PDF to obtain the actual distance information, not just the PDF shape estimation. More specifically, the proposed methods use the mean calculated from observations selected by statistical testing because the mean estimate generally outperforms the mode estimate. In addition, the Rao test in the context of the two-mode Gaussian mixture model (GMM) is demonstrated to be uniformly most powerful (UMP) test. Furthermore, the conditional variance of the range measurement is derived. Also, the proposed techniques outperforms that of competing algorithms in terms of localization accuracy.
- Files in This Item
-
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188429)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.