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Bayesian Kalman filtering, regularization and compressed sampling

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3 Author(s)
Chan, S.C. ; Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China ; Liao, B. ; Tsui, K.M.

Bayesian Kalman filter (BKF) is an important tool in signal processing, communications, control and statistics. This paper briefly reviews the principle of BKF for Gaussian mixture and proposes a new and efficient method for real-time implementation. Moreover, the close relationship between conventional KF and regularization theory in estimation is reviewed. Using this framework, the problem of sampling, smoothing and interpolation can be treated in a unified framework. New results on under-sampling using non-uniform samples will be presented.

Published in:

Circuits and Systems (MWSCAS), 2011 IEEE 54th International Midwest Symposium on

Date of Conference:

7-10 Aug. 2011