A New Application of Kalman Filtering Algorithm Based on Interval Calculation in Navigation System

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In traditional Kalman filtering algorithm, the system noise and observation noise should be assumed as zero-mean Gaussian white noise, meanwhile need the state-space model and relevant references be given and accurate. However, the white noise is just an ideal noise model that doesnt exist in real environment. This paper analyzed the effect to filtering result from the statistical estimation in traditional Kalman filtering algorithm and brought interval calculation into traditional Kalman filtering algorithm, which based on the concept of interval and could improve the robustness of the system, decrease the error caused by the statistical estimation of noise model.

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465-469

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April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] R. K. Mehra, Approaches to adaptive filtering, IEEE Trans. on Automatic Control, Vol. 17, pp.693-698(1972).

DOI: 10.1109/tac.1972.1100100

Google Scholar

[2] LiZhenYing, ShenYi, HuHengZhang, Kalman filtering algorithm with unknown time-varying noise system, System engineering & electronic technique, Vo l122, No11 (2000).

Google Scholar

[3] R. E Kalman , A new approach to linear filtering and prediction problems, Transactions of the ASME, Ser. D., Journal of Basic Engineering, 82, 34-45(1960).

Google Scholar

[4] Erik Cuevas1, 2, Daniel Zaldivar1, 2 and Raul Rojas1, Kalman filter for vision tracking, 10th August 2005, Freie Universität Berlin, Institut für Informatik Takustr. 9, D-14195 Berlin, Germany Universidad de Guadalajara.

Google Scholar

[5] Moore R.E., Kearfott R.B., Cloud M.J. Introduction to interval analysis (SIAM, 2009).

Google Scholar

[6] MaYunFeng, Application of interval Kalman filtering algorithm in navigation data combination, WeiFang college journal vol 6 No. 6 Nov. (2006).

Google Scholar

[7] Yunxiaokun, Yuanjianping, Interval Kalman filtering algorithm and its application in base navigation system, Northwest industry University journal, Vol. 23 No. 1 Feb. (2005).

Google Scholar

[8] HeXiuFeng, Yangguang, Kalman filter and its application in GPS/INS, vol. 33, No. 1 Feb., (2004).

Google Scholar

[9] Interval Krawczyk and Newton method. February 20, 2007, http: /www. ii. uj. edu. pl/~zgliczyn/cap07/krawczyk. pdf.

Google Scholar