Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths
Ubiquitous global positioning is not feasible by GNSS alone, as it lacks accurate position fixes in dense urban centres and indoors. Hybrid positioning methods have been developed to aid GNSS in those environments. Fingerprinting localization in wireless local area networks (WLANs) is a promising ai...
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doaj-039a99b98f8648979226fc0fd7ef6d042021-07-02T03:12:36ZengHindawi LimitedMobile Information Systems1574-017X1875-905X2017-01-01201710.1155/2017/82607468260746Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal StrengthsPhilipp Richter0Manuel Toledano-Ayala1Universidad Autónoma de Querétaro, Santiago de Querétaro, QRO, MexicoUniversidad Autónoma de Querétaro, Santiago de Querétaro, QRO, MexicoUbiquitous global positioning is not feasible by GNSS alone, as it lacks accurate position fixes in dense urban centres and indoors. Hybrid positioning methods have been developed to aid GNSS in those environments. Fingerprinting localization in wireless local area networks (WLANs) is a promising aiding system because of its availability, accuracy, and error mechanisms opposed to that of GNSS. This article presents a low-cost approach to ubiquitous, seamless positioning based on a particle filter integrating GNSS pseudoranges and WLAN received signal strength indicators (RSSIs). To achieve accurate location estimates indoors/outdoors and in the transition zones, appropriate likelihood functions are essential as they determine the influence of each sensor information on the position estimate. We model the spatial RSSI distributions with Gaussian processes and use these models to predict RSSIs at the particle’s positions to obtain point estimates of the RSSI likelihood function. The particle filter’s performance is assessed with real data of two test trajectories in an environment challenging for GNSS and WLAN fingerprinting localization. Outcomes of an extended Kalman filter using pseudoranges and a WLAN position as observation is included as benchmark. The proposed algorithm achieves accurate and robust seamless localization with a median accuracy of five meters.http://dx.doi.org/10.1155/2017/8260746 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Philipp Richter Manuel Toledano-Ayala |
spellingShingle |
Philipp Richter Manuel Toledano-Ayala Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths Mobile Information Systems |
author_facet |
Philipp Richter Manuel Toledano-Ayala |
author_sort |
Philipp Richter |
title |
Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths |
title_short |
Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths |
title_full |
Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths |
title_fullStr |
Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths |
title_full_unstemmed |
Ubiquitous and Seamless Localization: Fusing GNSS Pseudoranges and WLAN Signal Strengths |
title_sort |
ubiquitous and seamless localization: fusing gnss pseudoranges and wlan signal strengths |
publisher |
Hindawi Limited |
series |
Mobile Information Systems |
issn |
1574-017X 1875-905X |
publishDate |
2017-01-01 |
description |
Ubiquitous global positioning is not feasible by GNSS alone, as it lacks accurate position fixes in dense urban centres and indoors. Hybrid positioning methods have been developed to aid GNSS in those environments. Fingerprinting localization in wireless local area networks (WLANs) is a promising aiding system because of its availability, accuracy, and error mechanisms opposed to that of GNSS. This article presents a low-cost approach to ubiquitous, seamless positioning based on a particle filter integrating GNSS pseudoranges and WLAN received signal strength indicators (RSSIs). To achieve accurate location estimates indoors/outdoors and in the transition zones, appropriate likelihood functions are essential as they determine the influence of each sensor information on the position estimate. We model the spatial RSSI distributions with Gaussian processes and use these models to predict RSSIs at the particle’s positions to obtain point estimates of the RSSI likelihood function. The particle filter’s performance is assessed with real data of two test trajectories in an environment challenging for GNSS and WLAN fingerprinting localization. Outcomes of an extended Kalman filter using pseudoranges and a WLAN position as observation is included as benchmark. The proposed algorithm achieves accurate and robust seamless localization with a median accuracy of five meters. |
url |
http://dx.doi.org/10.1155/2017/8260746 |
work_keys_str_mv |
AT philipprichter ubiquitousandseamlesslocalizationfusinggnsspseudorangesandwlansignalstrengths AT manueltoledanoayala ubiquitousandseamlesslocalizationfusinggnsspseudorangesandwlansignalstrengths |
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