Robust Localization Method Based on Non-Parametric Probability Density Estimation
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. There...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10154033/ |
| _version_ | 1797796556799213568 |
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| author | Chee-Hyun Park Joon-Hyuk Chang |
| author_facet | Chee-Hyun Park Joon-Hyuk Chang |
| author_sort | Chee-Hyun Park |
| collection | DOAJ |
| description | 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 <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-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. |
| first_indexed | 2024-03-13T03:34:48Z |
| format | Article |
| id | doaj.art-cb1bb3af1aca4b2e9150d8897acd7137 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T03:34:48Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cb1bb3af1aca4b2e9150d8897acd71372023-06-23T23:00:44ZengIEEEIEEE Access2169-35362023-01-0111614686148010.1109/ACCESS.2023.328714010154033Robust Localization Method Based on Non-Parametric Probability Density EstimationChee-Hyun Park0https://orcid.org/0000-0002-9739-5277Joon-Hyuk Chang1https://orcid.org/0000-0003-2610-2323Department of Electronics Engineering, Hanyang University, Seoul, South KoreaDepartment of Electronics Engineering, Hanyang University, Seoul, South KoreaThis 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 <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-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.https://ieeexplore.ieee.org/document/10154033/First peakGaussian mixture modelk-nearest neighborlocalizationnon-line-of-sightorthogonal series |
| spellingShingle | Chee-Hyun Park Joon-Hyuk Chang Robust Localization Method Based on Non-Parametric Probability Density Estimation IEEE Access First peak Gaussian mixture model k-nearest neighbor localization non-line-of-sight orthogonal series |
| title | Robust Localization Method Based on Non-Parametric Probability Density Estimation |
| title_full | Robust Localization Method Based on Non-Parametric Probability Density Estimation |
| title_fullStr | Robust Localization Method Based on Non-Parametric Probability Density Estimation |
| title_full_unstemmed | Robust Localization Method Based on Non-Parametric Probability Density Estimation |
| title_short | Robust Localization Method Based on Non-Parametric Probability Density Estimation |
| title_sort | robust localization method based on non parametric probability density estimation |
| topic | First peak Gaussian mixture model k-nearest neighbor localization non-line-of-sight orthogonal series |
| url | https://ieeexplore.ieee.org/document/10154033/ |
| work_keys_str_mv | AT cheehyunpark robustlocalizationmethodbasedonnonparametricprobabilitydensityestimation AT joonhyukchang robustlocalizationmethodbasedonnonparametricprobabilitydensityestimation |