Kalman-Based Periodic Coefficient Update for FIR Adaptive Filters

TitleKalman-Based Periodic Coefficient Update for FIR Adaptive Filters
Publication TypeConference Paper
Year of Publication2007
AuthorsAvesta, N., and T. Aboulnasr
Conference NameMultimedia and Expo, 2007 IEEE International Conference on
Pagination735 -738
Date Publishedjul.
Keywordsadaptive filters, feedback, filter coefficients, FIR filters, Kalman background engine, Kalman feedback, Kalman filters, Kalman-based periodic coefficient, M-Tap periodic update LMS, nonstationary system identification, partial update algorithm

This paper presents a novel partial update algorithm for FIR adaptive filters based on a Kalman background engine. In the proposed system, a Kalman filter is setup with the coefficients of the full adaptive filter as the states to be estimated. The observation of the Kalman filter is the subset of the coefficients of the adaptive FIR filter being updated. It is shown that this setup allows for an improved estimation of the full set of filter coefficients despite the partial update. We propose two methods for postmortem improvements on an ordinary M-Tap periodic update LMS. We also propose a Kalman feedback method, in conjunction with a 1-Tap periodic update TMS, which has a similar performance to a full length LMS, for non-stationary system identification.


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