An ultrasonic positioning algorithm based on maximum correntropy criterion extended Kalman filter weighted centroid
- 167 Downloads
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.
KeywordsNon-Gaussian noise Maximum correntropy criterion (MCC) Extended Kalman filter (EKF) Weighted centroid Ultrasound
This research is supported by National Key R&D Program of China (2016YFB0700500). The authors would like to thank the reviewers for their comments.
- 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
- 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)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)Google Scholar
- 8.Jimnez, A.R., Seco, F.: Ultrasonic positioning methods for accurate positioning. Instituto de Automatica Industrial, Madrid (2005)Google Scholar
- 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)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)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)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)Google Scholar
- 21.Wang, P., et al.: Adaptive time delay estimation algorithm for indoor near-field electromagnetic ranging. Int. J. Commun. Syst. 30(5), (2017)Google Scholar