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An ultrasonic positioning algorithm based on maximum correntropy criterion extended Kalman filter weighted centroid

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Abstract

Ultrasonic positioning technology is being used in a wide range of application areas. In an ultrasonic positioning system, the noise of an ultrasound wave may not follow a Gaussian distribution but has a strong impulse because of many factors. A traditional extended Kalman filter based on the minimum mean square error would produce a linear estimation error and cannot handle a non-Gaussian noise effectively. Therefore, we propose a novel maximum correntropy criterion extended Kalman filter weighted centroid positioning algorithm based on a new Kalman gain formula to determine the maximum correntropy criterion. The maximum correntropy criterion maps the signal to a high-dimensional space and effectively deals with the non-Gaussian noise in ultrasonic positioning. In addition, the weighted centroid uses the results of the extended Kalman filter as inputs and reduces the impact of the linear estimation error on the positioning results. Experimental results show that the maximum correntropy criterion extended Kalman filter weighted centroid algorithm can improve the positioning accuracy by 60.06% over the extended Kalman filter and 22.83% compared with the maximum correntropy criterion extended Kalman filter. Overall, the proposed algorithm is more robust and effective.

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References

  1. Xu, C., He, J., Zhang, X.: Geometrical kinematic modeling on human motion using method of multi-sensor fusion. Information Fusion. 41(5), 243–254 (2017a)

    Google Scholar 

  2. Xu, C., He, J., Zhang, X.: Toward near-ground localization: modeling and applications for TOA ranging error. IEEE Trans. Antennas Propagation. 65(10), 5658–5662 (2017b)

    Article  Google Scholar 

  3. Kumar, A.A., Krishna, M.S.: Target tracking in a WSN with directional sensors using electronic beam steering. In: 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS). IEEE (2012)

  4. Mahfouz, S., et al.: Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sens. J. 14(10), 3715–3725 (2014)

    Article  Google Scholar 

  5. Reif, K., Unbehauen, R.: The extended Kalman filter as an exponential observer for nonlinear systems. IEEE Trans. Signal Process. 47(8), 2324–2328 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  6. Yang, P.: Efficient particle filter algorithm for ultrasonic sensor-based 2D range-only simultaneous localisation and mapping application. IET Wirel. Sensor Syst. 2(4), 394–401 (2012)

    Article  Google Scholar 

  7. Fakoorian, S.A. et al.: Ground reaction force estimation in prosthetic legs with an extended Kalman filter. In: 2016 Annual Systems Conference (SysCon). IEEE (2016)

  8. Jimnez, A.R., Seco, F.: Ultrasonic positioning methods for accurate positioning. Instituto de Automatica Industrial, Madrid (2005)

  9. Reza, I. et al.: Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise. In: 2016 Annual Conference on Information Science and Systems (CISS). IEEE pp. 500–505 (2016)

  10. Chen, B., Liu, X., Zhao, H., Principe, J.C.: Maximum correntropy Kalman filter. Automatica 76, 70–77 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  11. X. Liu, H. Qu, J. Zhao, B. Chen.: Extended Kalman filter under maximum correntropy criterion. In: Proceedings of the 2016 International Joint Conference on Neural Networks, Vancouver, Canada. pp. 1733–1737 (2016)

  12. Menegaz, H.M.T.: A systematization of the unscented Kalman filter theory. IEEE Trans. Autom. Control 60(10), 2583–2598 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  13. Liu, X., Chen, B., Xu, B., Wu, Z., Honeine, P.: Maximum correntropy unscented filter. Int. J. Syst. Sci. 48(8), 1607–1615 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  14. Wang, G., Li, N., Zhang, Y.: Maximum correntropy unscented Kalman and information filters for non-Gaussian measurement noise. J. Franklin Inst. 354(18), 8659–8677 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kim, S.J., Kim, B.K.: Dynamic ultrasonic hybrid localization system for indoor mobile robots. IEEE Trans. Ind. Electron. 60(10), 4562–4573 (2013)

    Article  Google Scholar 

  16. Liu, W., Pokharel, P.P., Prncipe, J.C.: Correntropy: properties and applications in non-Gaussian signal processing. IEEE Trans. Signal Process. 55(11), 5286–5298 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  17. Wang, J., et al.: Performance analysis of weighted centroid algorithm for primary user localization in cognitive radio networks. In: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR). IEEE, pp. 966–970 (2010)

  18. Wang, J., et al.: Weighted centroid localization algorithm: theoretical analysis and distributed implementation. IEEE Trans. Wirel. Commun. 10(10), 3403–3413 (2011)

    Article  Google Scholar 

  19. Julier, S.J., Jeffrey, K.U.: A new extension of the Kalman filter to nonlinear systems. Int. Symp. Aerosp. Def. Sens. Simul. Controls. 3(26), 182–193 (1997)

    Google Scholar 

  20. Cinar, G.T., Prncipe, J.C.: Hidden state estimation using the Correntropy Filter with fixed point update and adaptive kernel size. In: The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE pp. 1–6 (2012)

  21. Wang, P., et al.: Adaptive time delay estimation algorithm for indoor near-field electromagnetic ranging. Int. J. Commun. Syst. 30(5), (2017)

  22. Rao, M., et al.: A test of independence based on a generalized correlation function. Signal Process. 91(1), 15–27 (2011)

    Article  MATH  Google Scholar 

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Acknowledgements

This research is supported by National Key R&D Program of China (2016YFB0700500). The authors would like to thank the reviewers for their comments.

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Authors and Affiliations

  1. University of Science and Technology Beijing, Beijing, China

    Fuqiang Ma, Fangjie Liu, Xiaotong Zhang, Peng Wang, Hongying Bai & Hang Guo

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  1. Fuqiang Ma
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  2. Fangjie Liu
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  3. Xiaotong Zhang
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  4. Peng Wang
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  5. Hongying Bai
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  6. Hang Guo
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Correspondence to Xiaotong Zhang.

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Ma, F., Liu, F., Zhang, X. et al. An ultrasonic positioning algorithm based on maximum correntropy criterion extended Kalman filter weighted centroid. SIViP 12, 1207–1215 (2018). https://doi.org/10.1007/s11760-018-1272-2

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  • DOI: https://doi.org/10.1007/s11760-018-1272-2

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