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ISPRS Int. J. Geo-Inf. 2016, 5(12), 224; doi:10.3390/ijgi5120224

An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization

1
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
3
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editors: Georg Gartner, Haosheng Huang and Wolfgang Kainz
Received: 15 July 2016 / Revised: 7 November 2016 / Accepted: 24 November 2016 / Published: 30 November 2016
(This article belongs to the Special Issue Location-Based Services)
View Full-Text   |   Download PDF [4044 KB, uploaded 30 November 2016]   |  

Abstract

Location-based services (LBS) are services offered through a mobile device that take into account a device’s geographical location. To provide position information for these services, location is a key process. GNSS (Global Navigation Satellite System) can provide sub-meter accuracy in open-sky areas using satellite signals. However, for indoor and dense urban environments, the accuracy deteriorates significantly because of weak signals and dense multipaths. The situation becomes worse in indoor environments where the GNSS signals are unreliable or totally blocked. To improve the accuracy of indoor positioning for location-based services, an improved WiFi/Pedestrian Dead Reckoning (PDR) integrated positioning and navigation system using an adaptive and robust filter is presented. The adaptive filter is based on scenario and motion state recognition and the robust filter is based on the Mahalanobis distance. They are combined and used in the WiFi/PDR integrated system to weaken the effect of gross errors on the dynamic and observation models. To validate their performance in the WiFi/PDR integrated system, a real indoor localization experiment is conducted. The results indicate that the adaptive filter is better able to adapt to the circumstances of the dynamic model by adjusting the covariance of the process noise and the robust Kalman filter is able to mitigate the harmful effect of gross errors from the WiFi positioning. View Full-Text
Keywords: WiFi; pedestrian dead reckoning; integrated system; indoor localization; adaptive and robust filter WiFi; pedestrian dead reckoning; integrated system; indoor localization; adaptive and robust filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Z.; Liu, C.; Gao, J.; Li, X. An Improved WiFi/PDR Integrated System Using an Adaptive and Robust Filter for Indoor Localization. ISPRS Int. J. Geo-Inf. 2016, 5, 224.

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