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An improved PDR system with accurate heading and step length estimation using handheld smartphone

Published online by Cambridge University Press:  30 July 2021

Dayu Yan Open the ORCID record for Dayu Yan [Opens in a new window] ,
Chuang Shi  and
Tuan Li
Dayu Yan
Affiliation:
Beihang University School of Electronic and Information Engineering, Beijing 100083, China
Chuang Shi
Affiliation:
School of Electronic and Information Engineering, Beihang University, Beijing, China
Tuan Li*
Affiliation:
Beihang University School of Electronic and Information Engineering, Beijing 100083, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

Pedestrian dead reckoning (PDR) is widely used in handheld indoor positioning systems. However, low-cost inertial sensors built into smartphones provide poor-quality measurements, resulting in cumulative error which consists of heading estimation error caused by gyroscope and step length estimation error caused by an accelerometer. Learning more motion features through limited measurements is important to improve positioning accuracy. This paper proposes an improved PDR system using smartphone sensors. Using gyroscope, two motion patterns, walking straight or turning, can be recognised based on dynamic time warp (DTW) and thus improve heading estimation from an extended Kalman filter (EKF). Joint quasi-static field (JQSF) detection is used to avoid bad magnetic measurements due to magnetic disturbances in an indoor environment. In terms of periodicity of angular rate while walking, peak–valley angular velocity detection and zero-cross detection is combined to detect steps. A step-length estimation method based on deep belief network (DBN) is proposed. Experimental results demonstrate that the proposed PDR system can achieve more accurate indoor positioning.

Keywords

Indoor positioningpedestrian dead reckoning (PDR)Kalman filterdeep belief networksmartphone
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 1 , January 2022 , pp. 141 - 159
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Abadleh, A., Al-Hawari, E., Alkafaween, E. A. and Al-Sawalqah, H. (2017). Step Detection Algorithm for Accurate Distance Estimation Using Dynamic Step Length. In 2017 18th IEEE International Conference on Mobile Data Management (MDM). IEEE, pp. 324–327.CrossRefGoogle Scholar
Afzal, M. H., Renaudin, V. and Lachapelle, G. (2011). Use of earth's magnetic field for mitigating gyroscope errors regardless of magnetic perturbation. Sensors, 11(12), 11390–11414.CrossRefGoogle ScholarPubMed
Asraf, O., Shama, F. and Klein, I. (2021). PDRNet: A deep-learning pedestrian dead reckoning framework. IEEE Sensors Journal, 1–1. doi:10.1109/jsen.2021.3066840Google Scholar
Beauregard, S. and Haas, H. (2006). Pedestrian Dead Reckoning: A Basis for Personal Positioning. In Proceedings of the 3rd Workshop on Positioning, Navigation and Communication, pp. 27–35.Google Scholar
Borenstein, J. and Ojeda, L. (2010). Heuristic drift elimination for personnel tracking systems. Journal of Navigation, 63(4), 591–606. doi:10.1017/s0373463310000184CrossRefGoogle Scholar
Brajdic, A. and Harle, R. (2013). Walk Detection and Step Counting on Unconstrained Smartphones. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 225–234.CrossRefGoogle Scholar
Goyal, P., Ribeiro, V. J., Saran, H. and Kumar, A. (2011). Strap-down Pedestrian Dead-Reckoning System. In 2011 International Conference on Indoor Positioning and Indoor Navigation. IEEE, pp. 1–7.CrossRefGoogle Scholar
Gu, F., Khoshelham, K., Shang, J., Yu, F. and Wei, Z. (2017). Robust and accurate smartphone-based step counting for indoor localization. IEEE Sensors Journal, 17(11), 3453–3460.CrossRefGoogle Scholar
Gu, F., Khoshelham, K., Yu, C. and Shang, J. (2018). Accurate step length estimation for pedestrian dead reckoning localization using stacked autoencoders. IEEE Transactions on Instrumentation and Measurement, 68(8), 2705–2713.CrossRefGoogle Scholar
Ho, N.-H., Truong, P. H. and Jeong, G.-M. (2016). Step-detection and adaptive step-length estimation for pedestrian dead-reckoning at various walking speeds using a smartphone. Sensors, 16(9), 1423.CrossRefGoogle ScholarPubMed
Hu, J. and Sun, K. (2015). A robust orientation estimation algorithm using MARG sensors. IEEE Transactions on Instrumentation & Measurement, 64(3), 815–822.Google Scholar
Hua, Y., Guo, J. and Zhao, H. (2015). Deep Belief Networks and Deep Learning. In Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things. IEEE, pp. 1–4.Google Scholar
Kang, W. and Han, Y. (2014). SmartPDR: Smartphone-based pedestrian dead reckoning for indoor localization. IEEE Sensors Journal, 15(5), 2906–2916.CrossRefGoogle Scholar
Krizhevsky, A. and Hinton, G. (2009). Learning multiple layers of features from tiny images.Google Scholar
Lee, J.-S. and Huang, S.-M. (2019). An experimental heuristic approach to multi-pose pedestrian dead reckoning without using magnetometers for indoor localization. IEEE Sensors Journal, 19(20), 9532–9542.CrossRefGoogle Scholar
Li, H. and Yang, L. (2013). Accurate and fast dynamic time warping. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M. and Wang, W. (eds.). International Conference on Advanced Data Mining and Applications, Berlin, Heidelberg: Springer, 133–144.CrossRefGoogle Scholar
Oshman, Y. and Carmi, A. (2006). Attitude estimation from vector observations using a genetic-algorithm-embedded quaternion particle filter. Journal of Guidance, Control, and Dynamics, 29(4), 879–891.CrossRefGoogle Scholar
Poulose, A., Senouci, B. and Han, D. S. (2019). Performance analysis of sensor fusion techniques for heading estimation using smartphone sensors. IEEE Sensors Journal, 19(24), 12369–12380.CrossRefGoogle Scholar
Randell, C., Djiallis, C. and Muller, H. (2003). Personal Position Measurement Using Dead Reckoning. In Proceedings in Seventh IEEE International Symposium on Wearable Computers. IEEE, pp. 166–173.CrossRefGoogle Scholar
Tao, X., Zhang, X., Zhu, F., Wang, F. and Teng, W. (2018). Precise displacement estimation from time-differenced carrier phase to improve PDR performance. IEEE Sensors Journal, 18(20), 8238–8246.CrossRefGoogle Scholar
Tian, Q., Salcic, Z., Kevin, I., Wang, K. and Pan, Y. (2015). A multi-mode dead reckoning system for pedestrian tracking using smartphones. IEEE Sensors Journal, 16(7), 2079–2093.CrossRefGoogle Scholar
Vezočnik, M. and Juric, M. B. (2018). Average step length estimation models’ evaluation using inertial sensors: A review. IEEE Sensors Journal, 19(2), 396–403.CrossRefGoogle Scholar
Wang, J.-H., Ding, J.-J., Chen, Y. and Chen, H.-H. (2012) Real Time Accelerometer-Based Gait Recognition Using Adaptive Windowed Wavelet Transforms. In 2012 IEEE Asia Pacific Conference on Circuits and Systems. IEEE, pp. 591–594.CrossRefGoogle Scholar
Wang, G., Wang, X., Nie, J. and Lin, L. (2019). Magnetic-Based indoor localization using smartphone via a fusion algorithm. IEEE Sensors Journal, 19(15), 6477–6485. doi: 10.1109/jsen.2019.2909195CrossRefGoogle Scholar
Wang, Q., Luo, H., Ye, L., Men, A., Zhao, F., Huang, Y. and Ou, C. (2020). Personalized stride-length estimation based on active online learning. IEEE Internet of Things Journal, 7(6), 4885–4897.CrossRefGoogle Scholar
Weinberg, H. (2002). Using the ADXL202 in pedometer and personal navigation applications. Analog Devices. AN-602 application note, 2(2), 1–6.Google Scholar
Wu, J. (2020). MARG Attitude Estimation Using Gradient-Descent Linear Kalman Filter. IEEE Transactions on Automation Science and Engineering.CrossRefGoogle Scholar
Yao, Y., Pan, L., Feng, W., Xu, X., Liang, X. and Xu, X. (2020). A robust step detection and stride length estimation for pedestrian dead reckoning using a smartphone. IEEE Sensors Journal, 20(17), 9685–9697.CrossRefGoogle Scholar
Yin, H., Guo, H. and Deng, X. (2014). IMU indoor pedestrian dead reckoning research based on foot-mounted. Science of Surveying and Mapping, 39, 20–23.Google Scholar
Yuan, X., Yu, S., Zhang, S., Wang, G. and Liu, S. (2015). Quaternion-based unscented Kalman filter for accurate indoor heading estimation using wearable multi-sensor system. Sensors, 15(5), 10872–10890.CrossRefGoogle ScholarPubMed