Abstract
Location based service (LBS) becomes increasingly popular in indoor environments recently. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limiting problems. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weighted and suitable for real-time implementation by running an Android app designed for experiment. A comparison has been made between LaP and PDR. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 1.68 m.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61572448, 61170258, 61379127, 61103196, 61379128, and by the Shandong Provincial Natural Science Foundation, China under Grant No. ZR2014JL043. Xi Wang and Mingxing Jiang contributed equally to this work and should be regarded as co-first authors. The corresponding author is Zhongwen Guo.
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Wang, X., Jiang, M., Guo, Z., Hu, N., Sun, Z., Liu, J. (2016). LaP: Landmark-Aided PDR on Smartphones for Indoor Mobile Positioning. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_11
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DOI: https://doi.org/10.1007/978-3-319-42553-5_11
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