Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-25T01:59:45.251Z Has data issue: false hasContentIssue false
  • English
  • Français

Foot-mounted Pedestrian Navigation based on Particle Filter with an Adaptive Weight Updating Strategy

Published online by Cambridge University Press:  31 July 2014

Yang Gu ,
Qian Song ,
Yanghuan Li  and
Ming Ma
Yang Gu*
Affiliation:
(National University of Defence Technology)
Qian Song
Affiliation:
(National University of Defence Technology)
Yanghuan Li
Affiliation:
(National University of Defence Technology)
Ming Ma
Affiliation:
(National University of Defence Technology)
*
(E-mail: sunwheat1990@126.com)
Get access Rights & Permissions [Opens in a new window]

Abstract

The algorithm flow of an inertial-based Pedestrian Navigation System (PNS) can be divided into a trajectory-generation stage and trajectory-calibration stage. The Zero-velocity UPdaTe (ZUPT)-aided Extended Kalman Filter (EKF) algorithm is commonly used to resolve the trajectory of a walking person, but it still suffers from long-term drift. Many methods have been developed to suppress these drifts and thus to calibrate the trajectory generated by the previous stage. However, these methods have certain requirements, such as explicit map information or frequent location revisits, which are hard to satisfy in such situations as Search and Rescue (SAR) operations. A new approach is proposed in this paper that requires no explicit presupposition. This approach is based on a particle filter framework, with the weight of particles being adaptively adjusted according to the a priori knowledge of building structures and human behaviours. The distribution of particle weights is designed with awareness of the regular structures of buildings. The time-varying parameter of the distribution is acquired from a Hidden Markov Model (HMM) based on the foregoing odometry, which has a close relation with human behaviour. HMM is trained offline based on samples acquired in advance. Many real-world experiments under various scenarios were performed, and the results indicate good accuracy and robustness of the proposed approach.

Keywords

Inertial-based PNSParticle filterA priori knowledgeHMM
Type
Research Article
Information
The Journal of Navigation , Volume 68 , Issue 1 , January 2015 , pp. 23 - 38
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Angermann, M., Frassl, M., Doniec, M., Julian, B. J. and Robertson, P. (2012). Characterization of the indoor magnetic field for applications in localization and mapping. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on, 1-9. IEEEGoogle Scholar
Angermann, M. and Robertson, P. (2012). Footslam: Pedestrian simultaneous localization and mapping without exteroceptive sensors—hitchhiking on human perception and cognition. Proceedings of the IEEE, 100 (Special Centennial Issue), 1840–1848.CrossRefGoogle Scholar
Borenstein, J. and Ojeda, L. (2010). Heuristic drift elimination for personnel tracking systems. Journal of Navigation, 63, 591–606.CrossRefGoogle Scholar
Doucet, A. and Johansen, A.M. (2009). A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of Nonlinear Filtering, 12, 656–704.Google Scholar
Foxlin, E. (2005). Pedestrian tracking with shoe-mounted inertial sensors. Computer Graphics and Applications, IEEE, 25(6), 38–46.CrossRefGoogle ScholarPubMed
Godha, S. and Lachapelle, G. (2008). Foot mounted inertial system for pedestrian navigation. Measurement Science and Technology, 19(7), 075202.Google Scholar
Gu, Y., Ma, M., Li, Y. and Song, Q. (2013). Accurate height estimation based on a priori knowledge of buildings. In Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on, unpublished.Google Scholar
Hol, J.D., Schon, T.B., and Gustafsson, F. (2006). On resampling algorithms for particle filters. In Nonlinear Statistical Signal Processing Workshop, 2006, 79–82. IEEE.Google Scholar
Jimenez, A.R., Seco, F., Prieto, J.C. and Guevara, J. (2010). Indoor Pedestrian Navigation using an INS/EKF framework for Yaw Drift Reduction and a Foot-mounted IMU. In Positioning Navigation and Communication (WPNC), 2010 7th Workshop on, 135–143 ,IEEE.CrossRefGoogle Scholar
Kuusniemi, H., Bhuiyan, M.Z.H., Strom, M., Soderholm, S., Jokitalo, T., Chen, L. and Chen, R. (2012). Utilizing pulsed pseudolites and high-sensitivity GNSS for ubiquitous outdoor/indoor satellite navigation. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on 1-7. IEEE.CrossRefGoogle Scholar
Machaj, J. and Brida, P. (2012). Optimization of rank based fingerprinting localization algorithm. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on, 1-7. IEEE.CrossRefGoogle Scholar
Moghtadaiee, V. and Dempster, A. G. (2012). WiFi fingerprinting signal strength error modelling for short distances. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on, 1-6. IEEE.Google Scholar
Nilsson, J. O., Skog, I. and Händel, P. (2012). A note on the limitations of ZUPTs and the implications on sensor error modelling. In Proceeding of 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).Google Scholar
Patarot, A., Boukallel, M., and Lamy-Perbal, S. (2014). A Case Study On Sensors And Techniques For Pedestrian Inertial Navigation. In 1st IEEE International Symposium on Inertial Sensors and Systems 1-4.CrossRefGoogle Scholar
Pinchin, J., Hide, C. and Moore, T. (2012). A particle filter approach to indoor navigation using a foot mounted inertial navigation system and heuristic heading information, Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on, 1(10), 13–15.Google Scholar
Poritz, A. (1988). Hidden Markov models: A guided tour. In Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on, 7-13. IEEE.CrossRefGoogle Scholar
Saad, S.S. and Nakad, Z.S. (2011). A standalone RFID indoor positioning system using passive tags. Industrial Electronics, IEEE Transactions on, 58(5), 1961–1970.Google Scholar
Song, Q., Li, Y., Ma, M. and Zhou, Z. (2013). Low-cost miniaturized foot-mounted pedestrian navigation system Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on, unpublished.Google Scholar
Skog, I., Nilsson, J.O., and Handel, P. (2010). Evaluation of zero-velocity detectors for foot-mounted inertial navigation systems. In Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on 1-6. IEEE.Google Scholar
Thrun, S., Burgard, W., and Fox, D. (2005). Probabilistic robotics. MIT press.Google Scholar
Titterton, D., and Weston, J. (2004). Strapdown Inertial Navigation Technology. 2nd Edition. The Institution of Electronical Engineers, Reston USA.Google Scholar
Welch, L.R. (2003). Hidden Markov models and the Baum-Welch algorithm. IEEE Information Theory Society Newsletter, 53(4), 10–13.Google Scholar
Yu, Y., Yang, J., McKelvey, T. and Stoew, B. (2012). A compact UWB indoor and through-wall radar with precise ranging and tracking. International Journal of Antennas and Propagation, 2012.CrossRefGoogle Scholar