NLOS Identification and Mitigation for Localization
Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resol...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2011
|
| Online Access: | http://hdl.handle.net/1721.1/66704 https://orcid.org/0000-0002-8573-0488 |
| _version_ | 1826192822647128064 |
|---|---|
| author | Marano, Stefano Gifford, Wesley Michael Wymeersch, Henk Win, Moe Z. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Marano, Stefano Gifford, Wesley Michael Wymeersch, Henk Win, Moe Z. |
| author_sort | Marano, Stefano |
| collection | MIT |
| description | Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors. |
| first_indexed | 2024-09-23T09:29:43Z |
| format | Article |
| id | mit-1721.1/66704 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:29:43Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/667042022-09-30T14:45:33Z NLOS Identification and Mitigation for Localization Marano, Stefano Gifford, Wesley Michael Wymeersch, Henk Win, Moe Z. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Win, Moe Z. Marano, Stefano Gifford, Wesley Michael Wymeersch, Henk Win, Moe Z. Sensor networks can benefit greatly from location-awareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors. National Science Foundation (U.S.) (grant ECCS- 0901034) United States. Office of Naval Research (Presidential Early Career Award for Scientists and engineers (PECASE) N00014-09-1-0435) Defense University Research Instrumentation Program (U.S.) Defense University Research Instrumentation Program (U.S.) (grant N00014-08-1-0826) MIT/Army Institute for Soldier Nanotechnologies 2011-11-01T16:26:24Z 2011-11-01T16:26:24Z 2010-09 Article http://purl.org/eprint/type/JournalArticle 0733-8716 1558-0008 INSPEC Accession Number: 11523375 http://hdl.handle.net/1721.1/66704 Marano, Stefano et al. “NLOS identification and mitigation for localization based on UWB experimental data.” IEEE Journal on Selected Areas in Communications 28 (2010): 1026-1035. ©2010 IEEE. https://orcid.org/0000-0002-8573-0488 en_US http://dx.doi.org/10.1109/jsac.2010.100907 IEEE Journal on Selected Areas in Communications Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Marano, Stefano Gifford, Wesley Michael Wymeersch, Henk Win, Moe Z. NLOS Identification and Mitigation for Localization |
| title | NLOS Identification and Mitigation for Localization |
| title_full | NLOS Identification and Mitigation for Localization |
| title_fullStr | NLOS Identification and Mitigation for Localization |
| title_full_unstemmed | NLOS Identification and Mitigation for Localization |
| title_short | NLOS Identification and Mitigation for Localization |
| title_sort | nlos identification and mitigation for localization |
| url | http://hdl.handle.net/1721.1/66704 https://orcid.org/0000-0002-8573-0488 |
| work_keys_str_mv | AT maranostefano nlosidentificationandmitigationforlocalization AT giffordwesleymichael nlosidentificationandmitigationforlocalization AT wymeerschhenk nlosidentificationandmitigationforlocalization AT winmoez nlosidentificationandmitigationforlocalization |