Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach
Indoor tracking and navigation (ITN) mainly depend on indoor localization. An impulse radio ultra-wideband (IR-UWB) is the most advanced technology for precision indoor localization. Besides its precision, the IR-UWB also has low complex hardware, low power consumption, and a flexible data rate that...
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2019-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/8664475/ |
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| author | Sunil K. Meghani Muhammad Asif Faroq Awin Kemal Tepe |
| author_facet | Sunil K. Meghani Muhammad Asif Faroq Awin Kemal Tepe |
| author_sort | Sunil K. Meghani |
| collection | DOAJ |
| description | Indoor tracking and navigation (ITN) mainly depend on indoor localization. An impulse radio ultra-wideband (IR-UWB) is the most advanced technology for precision indoor localization. Besides its precision, the IR-UWB also has low complex hardware, low power consumption, and a flexible data rate that makes it the ideal candidate for ITN. However, two significant challenges impede the achievement of high-resolution accuracy and optimum performance: non-line-of-sight (NLOS) channel condition and multipath propagation (MPP). To enhance the performance under these conditions, the ranging error is estimated and corrected using parameters’ uncertainties. The uncertainties in the channel’s parameters have a relationship with the error, and these uncertainties are induced due to the NLOS and MPP propagation conditions. The parameters are collected in real-time experimental setups in two different environments. A proposed fuzzy inference model utilizes these uncertainties and the relationship to estimate ranging errors. The model is evaluated, and its performance is gauged in terms of residual ranging error cumulative distribution, root mean square error, and outage probability parameters using experimental measurements and compared with the state-of-the-art work. Moreover, the proposed fuzzy model is evaluated for computational complexity in terms of execution time and compared with the state-of-the-art work. The time is estimated on the targeted embedded system. The experimental and simulated results show that the proposed model effectively minimizes the ranging errors and computational burden. Moreover, the model does not induce a delay in estimating ranging error due to the non-statistical based solution. |
| format | Article |
| id | doaj-art-5019222b53a74e249e5d8a2166c0149f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5019222b53a74e249e5d8a2166c0149f2025-01-30T00:00:40ZengIEEEIEEE Access2169-35362019-01-017336863369710.1109/ACCESS.2019.29042018664475Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy ApproachSunil K. Meghani0https://orcid.org/0000-0001-6232-5431Muhammad Asif1Faroq Awin2Kemal Tepe3WiCIP Lab, University of Windsor, Windsor, CanadaElectrical Engineering Department, Faculty of Engineering Science and Technology, Ziauddin University, Karachi, PakistanWiCIP Lab, University of Windsor, Windsor, CanadaWiCIP Lab, University of Windsor, Windsor, CanadaIndoor tracking and navigation (ITN) mainly depend on indoor localization. An impulse radio ultra-wideband (IR-UWB) is the most advanced technology for precision indoor localization. Besides its precision, the IR-UWB also has low complex hardware, low power consumption, and a flexible data rate that makes it the ideal candidate for ITN. However, two significant challenges impede the achievement of high-resolution accuracy and optimum performance: non-line-of-sight (NLOS) channel condition and multipath propagation (MPP). To enhance the performance under these conditions, the ranging error is estimated and corrected using parameters’ uncertainties. The uncertainties in the channel’s parameters have a relationship with the error, and these uncertainties are induced due to the NLOS and MPP propagation conditions. The parameters are collected in real-time experimental setups in two different environments. A proposed fuzzy inference model utilizes these uncertainties and the relationship to estimate ranging errors. The model is evaluated, and its performance is gauged in terms of residual ranging error cumulative distribution, root mean square error, and outage probability parameters using experimental measurements and compared with the state-of-the-art work. Moreover, the proposed fuzzy model is evaluated for computational complexity in terms of execution time and compared with the state-of-the-art work. The time is estimated on the targeted embedded system. The experimental and simulated results show that the proposed model effectively minimizes the ranging errors and computational burden. Moreover, the model does not induce a delay in estimating ranging error due to the non-statistical based solution.https://ieeexplore.ieee.org/document/8664475/Fuzzy logicindoor tracking and navigationimpulse radio ultrawide bandlocalizationcomputational complexity |
| spellingShingle | Sunil K. Meghani Muhammad Asif Faroq Awin Kemal Tepe Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach IEEE Access Fuzzy logic indoor tracking and navigation impulse radio ultrawide band localization computational complexity |
| title | Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach |
| title_full | Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach |
| title_fullStr | Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach |
| title_full_unstemmed | Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach |
| title_short | Empirical Based Ranging Error Mitigation in IR-UWB: A Fuzzy Approach |
| title_sort | empirical based ranging error mitigation in ir uwb a fuzzy approach |
| topic | Fuzzy logic indoor tracking and navigation impulse radio ultrawide band localization computational complexity |
| url | https://ieeexplore.ieee.org/document/8664475/ |
| work_keys_str_mv | AT sunilkmeghani empiricalbasedrangingerrormitigationiniruwbafuzzyapproach AT muhammadasif empiricalbasedrangingerrormitigationiniruwbafuzzyapproach AT faroqawin empiricalbasedrangingerrormitigationiniruwbafuzzyapproach AT kemaltepe empiricalbasedrangingerrormitigationiniruwbafuzzyapproach |