Abstract
The geomagnetic field exists everywhere and has been demonstrated to be local disturbed and stable over time. In this paper, we present an approach for indoor positioning which does not rely upon any additional support equipment, using magnetic fingerprint collected by magnetic field sensors in smartphone and walking information of user measured by self-contained inertial sensors. In order to improve the practicability of our positioning method, we choose the Z-axis magnetic of world coordinate system as feature information, which can reach no restrain of phone’s gesture and orientation when pedestrians walk. Moreover, to improve the discernibility of geomagnetic information, we measure the geomagnetic signals as a sequence, and we match the geomagnetic signals with the established fingerprint using dynamic time warping (DTW) algorithm. Meanwhile, a hidden Markov model (HMM) based positioning process is adopted to strengthen the robustness of the algorithm. Extensive field tests have been conducted in an office building to verify the performance of proposed algorithm. Test results show that the proposed algorithm can achieve less than 1.11 ms of mean error and 2.13 ms of 95th percentile error.
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References
Liu D, Pei L, Qian J, et al (2016) Simplified ellipsoid fitting-based magnetometer calibration for pedestrian dead reckoning. In: China satellite navigation conference (CSNC) 2016 proceedings: Volume II
Liu H, Darabi H, Banerjee P, Liu J (2007) Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybern C Appl Rev 37(6):1067–1080
Kok M, Hol JD, Schön TB (2015) Indoor positioning using ultrawideband and inertial measurements. IEEE Trans Veh. Technol 64(4):1293–1303
Tomic S, Beko M, Dinis R (2015) RSS-based positioning in wireless sensor networks using convex relaxation: noncooperative and cooperative schemes. IEEE Trans Veh Technol 64(5):2037–2050
Chen Y, Lymberopoulos D, Liu J et al (2013) Indoor positioning using FM signals. IEEE Trans Mobile Comput 12(12):1502–1517
Haverinen J, Kemppainen A (2009) Global indoor self-positioning based on the ambient magnetic field. Robot Auton Syst 57(10):1028–1035
Shahidi S, Valaee S (2015) GIPSy: geomagnetic indoor positioning system for smartphones. International conference on indoor positioning and indoor navigation. IEEE
Ma Y, Dou Z, Jiang Q et al (2016) Basmag: an optimized HMM based positioning system using backward sequences matching algorithm exploiting geomagnetic information. 16(20):7472–7482
Wang Q, Luo H, Zhao F, Shao W (2016) An indoor self-localization algorithm using the calibration of the online magnetic fingerprints and indoor landmarks. In: 2016 international conference on indoor positioning and indoor navigation (IPIN)
Haverinen J, Kemppainen A (2009) Global indoor self-localization based on the ambient magnetic field. Robot Auton Syst 57(10):1028–1035
Qian J, Pei L, Ma J, Ying R, Liu P (2015) Vector graph assisted pedestrian dead reckoning using an unconstrained smartphone. Sensors 15(3):5032–5057
Qian J, Ma J, Ying R, Liu P (2013) RPNOS: reliable pedestrian navigation on a smartphone. In: Geo-informatics in resource management and sustainable ecosystem. Springer, Berlin Heidelberg
Li B, Gallagher T, Dempster AG, Rizos C (2012) How feasible is the use of magnetic field alone for indoor positioning. In: International conference on indoor positioning and indoor navigation, pp 1–9
Yun X, Bachmann ER, Mcghee RB (2008) A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements. IEEE Trans Instrum Meas 57(3):638–650
Kim B, Kong SH (2016) A novel indoor positioning technique using magnetic fingerprint difference. IEEE Trans Instrum Meas 1–11
Qian J, Ma J, Ying R, Liu P, Pei L (2013) An improved indoor localizaion method using smartphone inertial sensors. In: IPIN, pp 1–7
Subbu KP, Gozick B, Dantu R (2013) Locateme: magnetic-fields-based indoor localization using smartphones. ACM Trans Intell Syst Technol 4(4):99–103
Chen R, Pei L, Chen Y (2011) A smart phone based PDR solution for indoor navigation. In: Proceedings of the 24th international technical meeting of the satellite division of the institute of navigation (ION GNSS 2011), pp 1404–1408
Qian J, Pei L, Ying R, Chen X, Zou D, Liu P et al (2014) Continuous motion recognition for natural pedestrian dead reckoning using smartphone sensors. In: Proceedings of international technical meeting of the satellite division of the Institute of Navigation
Qian J, Pei L, Zou D, Liu P (2015) Optical flow-based gait modeling algorithm for pedestrian navigation using smartphone sensors. Sens J IEEE 15(12)6797–6804. doi:10.1109/JSEN.2015.2464696
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Mu, X., Qian, J., Xu, C., Pei, L., Liu, P., Yu, W. (2017). Magnetic Field Based Indoor Pedestrian Positioning Using Self-contained Sensors. In: Sun, J., Liu, J., Yang, Y., Fan, S., Yu, W. (eds) China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume II. CSNC 2017. Lecture Notes in Electrical Engineering, vol 438. Springer, Singapore. https://doi.org/10.1007/978-981-10-4591-2_46
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DOI: https://doi.org/10.1007/978-981-10-4591-2_46
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