Abstract
Radio frequency (RF) signal propagation suffers from time-varying fading effects, and thus radio map-based localization systems are hard to hold the expected accuracy. Base stations (BS)-based architectures show us the probable solutions to overcome the negative impacts by producing adaptive radio maps. In this chapter, the adaptive approach that is presented in our previous work is adopted. To further mitigate the impacts of dynamic environments, we propose a hybrid location estimation method that fuses WLAN signals and inertial data through the sequential importance resampling (SIR) Particle Filter (PF) algorithm. Experimental results suggest that the hybrid method can provide more accurate location tracking, compared to previous algorithms, such as K weighted nearest neighbors (KWNN), initial radio map-based PF, adaptive radio map-based PF, pedestrian dead reckoning (PDR). And it nearly costs equivalent computational time, compared to those radio map-based PF algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arulampalam S, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188
Bahl P, Padmanabhan VN (2000) RADAR: an in-building RF-based user location and tracking system. In: Proceedings of IEEE INFOCOM ‘00, p 775–784
Doucet A, Godsill S, Andrieu C (2000) On sequential Monte Carlo sampling methods for Bayesian filtering. Stat Comput 10(3):197–208
Gordon N, Salmond D, Smith A (1993) Novel approach to nonlinear/ non-Gaussian Bayesian state estimation. IEE Proc F Radar Signal Process 140(2):107–113
Herrera P (2009) Improving data fusion in user positioning systems. Dissertation, Universitat Jaume I, Castelló de la Plana, Spain
Judd T (1997) A personal dead reckoning module. Proc ION GPS 1997:47–51
Krishnan P, Krishnakumar AS, Ju WH et al (2004) A system for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks. In: Proceedings of IEEE INFOCOM ‘04, p 1001–1011
Levi R, Judd T (1999) Dead reckoning navigational system using accelerometer to measure foot impacts. US Patent 5,583,776, 10 Dec 1996
Ni LM, Liu Y, Lau YC et al (2003) LANDMARC: indoor location sensing using active RFID. In: Proceedings of 1st IEEE international conference on pervasive computing and communications, pp 407–415
Orr RJ, Abowd GD (2000) The smart floor: a mechanism for natural user identification and tracking. In: Proceedings of CHI ‘00 extended abstracts on human factors in computing systems, pp 275–276
Priyantha NB, Miu AKL, Balakrishnan H et al (2001) The cricket compass for context-aware mobile applications. In: Proceedings of 7th annual international conference on mobile computing and networking, pp 1–14
Toth C, Grejner-Brzezinska D, Moafipoor S (2007) Pedestrian tracking and navigation using neural networks and fuzzy logic. In: Proceedings of IEEE international symposium on intelligent signal processing, pp 1–6
Wang H, Ma L, Xu Y et al (2011) Dynamic radio map construction for WLAN indoor location. In: Proceedings of 3rd international conference on intelligent human-machine systems and cybernetics, pp 162–165
Want R, Hopper A, Falcao V et al (1992) The active badge location system. ACM Trans Inf Syst 10(1):91–102
Weinberg H (2002) Using the ADXL202 in pedometer and personal navigation applications. Application notes AN-602, Analog devices. http://www.analog.com/static/imported-files/application_notes/513772624AN602.pdf
Welch G, Bishop G (2001) An introduction to the kalman filter. University of North Carolina at Chapel Hill. http://ece.ut.ac.ir/classpages/S85/OptimalControl/books/kalman_filter_notes.pdf
Widyawan KM, Pesch D (2007) A Bayesian approach for RF-based indoor localisation. In: Proceedings of 4th international symposium on wireless communication systems, Trondheim, Norway. IEEE, Piscataway, USA, pp 133–137
Xia L, Wu D (2012) On realtime and adaptive indoor positioning method under multi-base-station mode. Bull Surv Mapp 11:1–6
Yin J, Yang Q, Ni LM (2008) Learning adaptive temporal radio map for signal-strength-based location estimation. IEEE Trans Mobile Comput 7(7):869–883
Acknowledgement
This study is supported by the funding from National Natural Science Foundation of China (41071284).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Wu, D., Xia, L., Mok, E. (2014). Hybrid Location Estimation by Fusing WLAN Signals and Inertial Data. In: Liu, C. (eds) Principle and Application Progress in Location-Based Services. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-04028-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-04028-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04027-1
Online ISBN: 978-3-319-04028-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)