Abstract
Pedestrian dead reckoning (PDR) and WiFi fingerprint localization technologies have been widely used in the field of indoor localization. To reduce the limitation of the single localization technology, the PDR/WiFi integration method has become a widely accepted indoor localization solution. For indoor pedestrian real-time tracking and localization, due to the short time available to collect the received signal strength (RSS) and the high fluctuation of RSS, using only the RSS measurements as the RSS information will cause great localization errors. Therefore, this paper proposes an improved PDR/WiFi integration method to address the fluctuation problem of RSS for indoor pedestrian localization. The experimental results show that the localization accuracy of the proposed method outperforms the traditional PDR/WiFi integration method.
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References
Yang X, Wang Y, Zhou M et al (2018) Pedestrian motion learning based indoor WLAN localization via spatial clustering. Wirel Commun Mob Comput 70(99):1–10
Ho N, Truong P, Jeong G (2016) Step-detection and adaptive step-length estimation for pedestrian dead-reckoning at various walking speeds using a smartphone. Sensors 16(9):1423
Bi J, Wang Y, Li Z et al (2019) Fast radio map construction by using adaptive path loss model interpolation in large-scale building. Sensors 19(3):712
He S, Lin W, Chan S (2016) Indoor localization and automatic fingerprint update with altered AP signals. IEEE Trans Mob Comput 99:1–12
Zampella F, Bahillo A, Prieto J et al (2013) Pedestrian navigation fusing inertial and RSS/TOF measurements with adaptive movement/measurement models: Experimental evaluation and theoretical limits. Sens Actuators A-Phys 203:249–260
Mazuelas S, Bahillo A, Lorenzo R et al (2009) Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks. IEEE J Sel Top Sign Proces 3(5):821–831
Wang J, Hu A, Liu C et al (2015) A floor-map-aided WiFi/pseudo-odometry integration algorithm for an indoor positioning system. Sensors 15(4):7096–7124
Li Z, Liu C, Gao J et al (2016) An improved WiFi/PDR integrated system using an adaptive and robust filter for indoor localization. ISPRS Int J Geo-inf 5(12):224
Deng Z, Wang G, Qin D et al (2016) Continuous indoor positioning fusing WiFi, smartphone sensors and landmarks. Sensors 16(9):1427
Wang B, Liu X, Yu B et al (2018) Pedestrian dead reckoning based on motion mode recognition using a smartphone. Sensors 18(6):1811
Gansemer S, Pueschel S, Frackowiak R et al (2010) Improved RSSI-based Euclidean distance positioning algorithm for large and dynamic WLAN environments. Computing 9(1):37–44
Xue W, Hua X, Li Q et al (2018) A new weighted algorithm based on the uneven spatial resolution of RSSI for indoor localization. IEEE Access 6:26588–26595
Acknowledgements
This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant HEUCF180801, and in part by the National Key Research and Development Plan of China under Grant 2016YFB0502100 and Grant 2016YFB0502103.
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Wang, B., Liu, X., Yu, B., Jia, R., Huang, L., Jia, H. (2020). An Improved PDR/WiFi Integration Method for Indoor Pedestrian Localization. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_128
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DOI: https://doi.org/10.1007/978-981-13-9409-6_128
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