DocumentCode :
3197654
Title :
Kalman-Based Periodic Coefficient Update for FIR Adaptive Filters
Author :
Avesta, Nastooh ; Aboulnasr, Tyseer
Author_Institution :
Ottawa Univ., Ottawa
fYear :
2007
fDate :
2-5 July 2007
Firstpage :
735
Lastpage :
738
Abstract :
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.
Keywords :
FIR filters; Kalman filters; adaptive filters; feedback; FIR filters; Kalman background engine; Kalman feedback; Kalman-based periodic coefficient; M-Tap periodic update LMS; adaptive filters; filter coefficients; nonstationary system identification; partial update algorithm; Adaptive filters; Engines; Error correction; Filtering; Finite impulse response filter; Gaussian noise; Information technology; Kalman filters; Least squares approximation; State estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo, 2007 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
1-4244-1016-9
Electronic_ISBN :
1-4244-1017-7
Type :
conf
DOI :
10.1109/ICME.2007.4284755
Filename :
4284755
Link To Document :
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