A Location-Estimation Experimental Platform Based on Error Propagation for Wireless Sensor Networks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 253)

Abstract

This paper presents a location-estimation experimental platform based on the error propagation approach to reduce the computational load of traditional algorithms. For the experimental platform with the scalar information, the proposed technique based on the Bayesian approach is handled by a state space model; a weighted technique with the reliability of the information passing is based on the error propagation law. As compared with a traditional Kalman filtering (KF) algorithm, the proposed algorithm has much lower computational complexity with the decoupling approach. Numerical simulations and experimental results show that the proposed location-estimation algorithm can achieve the location accuracy close to that of the KF algorithm.

Keywords

Bayesian filtering Error propagation Location estimation Wireless sensor networks ZigBee positioning system 

Notes

Acknowledgments

This work was supported in part by the National Science Council of the Republic of China (R.O.C.) under Grants NSC 101-2218-E-033-007 and NSC 101-2221-E-130 -017.

References

  1. 1.
    Rantakokko J, Rydell J, Stromback P, Handel P, Callmer J, Tornqvist D, Gustafsson F, Jobs M, Gruden M, Gezici M (2011) Accurate and reliable soldier and first responder indoor positioning: multisensor systems and cooperative localization. IEEE Wirel Commun 18(2):10–18CrossRefGoogle Scholar
  2. 2.
    Barton RJ, Zheng R, Gezici S, Veeravalli VV (2008) Signal processing for location estimation and tracking in wireless environments. EURASIP J Adv Signal Process 2008:1–3Google Scholar
  3. 3.
    Pahlavan K, Li X, Makela JP (2002) Indoor geolocation science and technology. IEEE Commun Mag 40:112–118CrossRefGoogle Scholar
  4. 4.
    Chiou Y-S, Wang C-L, Yeh S-C (2011) Reduced-complexity scheme using alpha-beta filtering for location tracking. IET Commun 5(13):1806–1813CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Chiou Y-S, Tsai F, Wang C-L, Huang C-T (2012) A reduced-complexity scheme using message passing for location tracking. EURASIP J Adv Signal Process 2012(1):18Google Scholar
  6. 6.
    Chiou Y-S, Wang C-L, Yeh S-C, Su M-Y (2009) Design of an adaptive positioning system based on WiFi radio signals. Elsevier Comput Commun 32:1245–1254Google Scholar
  7. 7.
    Chiou Y-S, Wang C-L, Yeh S-C (2010) An adaptive location estimator using tracking algorithms for indoor WLANs. ACM/Springer Wirel Netw 16(7):1987–2012Google Scholar
  8. 8.
    Chen B-S, Yang C-Y, Liao F-K, Liao J-F (2009) Mobile location estimator in a rough wireless environment using extended Kalman-based IMM and data fusion. IEEE Trans Veh Tech 58(3):1157–1169Google Scholar
  9. 9.
    Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50:174–188CrossRefGoogle Scholar
  10. 10.
    Fox D, Hightower J, Liao L, Schulz D, Borriello G (2003) Bayesian filtering for location estimation. IEEE Pervasive Comput 2(3):24–33CrossRefGoogle Scholar
  11. 11.
    Cappé O, Godsill SJ, Moulines E (2007) An overview of existing methods and recent advances in sequential Monte Carlo. Proc IEEE 95(5):899–924CrossRefGoogle Scholar
  12. 12.
    Koch K-R (1999) Parameter estimation and hypothesis testing in linear models. Springer, BerlinCrossRefMATHGoogle Scholar
  13. 13.
    Tsai F, Chiou Y-S, Chang H (2013) A positioning scheme combining location tracking with vision assisting for wireless sensor networks. J Appl Res Technol (in press) Google Scholar
  14. 14.
  15. 15.
    Farina A, Studer FA (1985) Radar data processing, vol 1: introduction and tracking. Research Studies Press, LetchworthGoogle Scholar
  16. 16.
    Wang C-L, Chiou Y-S, Tsai F (2013) Reduced-complexity tracking scheme based on adaptive weighting for location estimation. IET Commun (in press)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Yih-Shyh Chiou
    • 1
  • Sheng-Cheng Yeh
    • 2
  • Shang-Hung Wu
    • 2
  1. 1.Department of Electronic EngineeringChung Yuan Christian UniversityTaoyuanTaiwan
  2. 2.Department of Computer and Communication EngineeringMing Chuan UniversityTaoyuanTaiwan

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