Efficient adaptive FIR and IIR filters

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Author
Ben-Yakov, Uzi
Date
1979-12Advisor
Myers, G.A.
Panholzer, R.
Parker, Sydney R.
Wang, P.C.C.
Hamming, R.W.
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Show full item recordAbstract
Self adaptive filters adjust their parameters to perform
an almost optimal filtering operation without apriori knowledge
of the input signal statistics . Two approaches to the
design of efficient self adaptive discrete filtering algorithms
are considered. For non-recursive (FIR) adaptive filters, simplified estimations
of the gradient of the performance function to be
minimized are considered. These algorithms result in reduced
complexity of implementation, improved dynamic operating range
with about the same misad justment errors and convergence time
as the classic LMS (Lease Means Squared) algorithm. An analysis
of the simplified gradient approach is presented and confirmed
experimentally for the specific example of an adaptive
line enhancer (ALE) . The results are used to compare the
simplified gradient approaches with each other and the LMS
algorithm. This comparison is done using a new graphic presentation
of adaptive filter operating characteristics and a
complexity index. This comparison indicates that the simplified gradient estimators are superior to the LMS algorithm for
filters of equal complexity.
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