A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems


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This paper describes a new adaptive filtering approach for nonlinear systems with additive noise. Based on Square-Root Unscented Kalman Filter (SRUKF), the traditional Maybeck’s estimator is modified and extended to the nonlinear systems, the estimation of square root of the process noise covariance matrix Q or measurement noise covariance matrix R is obtained straightforwardly. Then the positive semi-definiteness of Q or R is guaranteed, some shortcomings of traditional Maybeck’s algorithm are overcome, so the stability and accuracy of the filter is improved greatly.



Edited by:

Ching Kuo Wang and Jing Guo




Y. Zhou et al., "A New Adaptive Square-Root Unscented Kalman Filter for Nonlinear Systems", Applied Mechanics and Materials, Vols. 300-301, pp. 623-626, 2013

Online since:

February 2013




[1] S.J. Julier, J.K. Uhlmann and H. Durrant-Whyte: IEEE Trans. on Automatic Control, Vol. 45(2000), p.477.

[2] R. van der Merwe and E.A. Wan: IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 6(2001), p.3461.

[3] R. van der Merwe and E.A. Wan: in Proceedings of European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium (2001).

[4] Peihua. Li and Tianwen Zhang: In Proceedings of Statistical Methods in Video Processing, June (2002).

[5] Ding. W, Wang. J and Rizos. C: The Journal of Navigation, Vol. 60(2007), p.517.

[6] D.J. Jwo and S.H. Wang: IEEE Sensors Journal, Vol. 7(2007) , No. 5, p.778.

[7] D.J. Lee and K.T. Alfriend: In: AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Rhode Island (2004), pp.1-20.

[8] Franz D. Busse: Ph.D. Dissertation, Stanford University (2003), pp.151-172.

[9] Yong Shi, Chongzhao Han and Yongqi Liang: 12th International Conference on Information Fusion, Seattle, WA, USA (2009), p.1815.