Design and Evaluation of an Improved Integration of WiFi Fingerprinting and MEMS Sensors in Smartphones

Yuan Zhuang, You Li, Haiyu Lan, and Naser El-Sheimy

Abstract: In this decade, the smartphone has become an ideal platform for indoor navigation applications. Much research has been done in this topic, however, smartphone-based indoor navigation is still challenging. This paper proposes an improved integration structure for WiFi fingerprinting and MEMS sensors for indoor navigation. The main contribution of this work is that the proposed method constrains the search space for WiFi fingerprinting, which improves the process speed and positioning accuracy for WiFi fingerprinting. In this method, the search space is estimated adaptively based on the position variance in the extended Kalman filtering and matched access point number in WiFi fingerprinting. The field experiments show that the proposed structure can achieve the positioning accuracy of 2.14 m - 3.68 m (RMS) only using crowdsourcing-based WiFi radio map database, which is much better than the MEMS-based navigation solution and traditional loosely-coupled integration solution.
Published in: Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015)
September 14 - 18, 2015
Tampa Convention Center
Tampa, Florida
Pages: 8 - 14
Cite this article: Zhuang, Yuan, Li, You, Lan, Haiyu, El-Sheimy, Naser, "Design and Evaluation of an Improved Integration of WiFi Fingerprinting and MEMS Sensors in Smartphones," Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015), Tampa, Florida, September 2015, pp. 8-14.
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