NDLI: Efficient narrowband FIR and IFIR filters based on powers-of-two sigma-delta coefficient truncation

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Efficient narrowband FIR and IFIR filters based on powers-of-two sigma-delta coefficient truncation

Content Provider	IEEE Xplore Digital Library
Author	Powell, S.R. Chau, P.M.
Copyright Year	1992
Abstract	A new quantization scheme is presented for truncating the values of a high precision digital sequence to only a few non-zero bits. Sigma-delta modulation is used to round values to the nearest n-bit canonic signed-digit code (number representable as sum or difference of at most n powers-of-two) such that the quantization error is moved to higher frequencies. This technique can be applied to the impulse response of an FIR filter to truncate the coefficient values to a few non-zero bits; eliminating the need for multiplications. The resulting filter's magnitude response will be accurate over low frequencies and is thus suitable for narrowband lowpass applications. Sigma-delta CSD truncation is applied to previous multiplier efficient approaches for narrowband filters (prefilter-equalizer, multirate) and examples are given which show an additional 4 to 64 times reduction in computational complexity. The sigma-delta CSD interpolated FIR (SCIFIR) filter is presented and examples are given which show an additional 29%-65% reduction in computational complexity over previously reported multiplier efficient IFIR designs.<>
Sponsorship	IEEE Circuits and Systems Society
Starting Page	497
Ending Page	505
Page Count	9
File Size	902135
File Format	PDF
ISSN	10577130
Volume Number	41
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Finite impulse response filter Narrowband Delta-sigma modulation Filtering Bandwidth Digital filters Computational complexity Quantization Frequency Digital signal processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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