Abstract
With the developing of mobile applications based on indoor location based services (LBS), the higher accuracy of indoor positioning is required. The Location Fingerprinting and Dead Reckoning based hybrid indoor positioning (HIP) algorithm is proposed to calculate the current indoor location more precisely. During the whole process of indoor positioning, WiFi modules and inertial sensors, which are mounted in smart devices, are used to obtain essential sensing data to position. HIP algorithm calculates the initial location through the weighted fingerprinting K nearest neighbor (WFKNN) algorithm using RSSI signals of WiFi firstly, and then starts to update the current location through both the WFKNN algorithm and the dead reckoning technique. The experiments are implemented several smart phones with Android system, the results show the HIP algorithm performs much better than KNN and dead reckoning algorithm on positioning accuracy.
This work is supported by the National Natural Science Foundation of China under Grant No. 61272529; the Fundamental Research Funds for the Central Universities under Grant No. N120417002, 2014.
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Yu, R., Wang, P., Zhao, Z. (2015). The Location Fingerprinting and Dead Reckoning Based Hybrid Indoor Positioning Algorithm. In: Sun, L., Ma, H., Fang, D., Niu, J., Wang, W. (eds) Advances in Wireless Sensor Networks. CWSN 2014. Communications in Computer and Information Science, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46981-1_57
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DOI: https://doi.org/10.1007/978-3-662-46981-1_57
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