NDLI: Nonlinear C-filters for nonstationary signals and deconvolution in impulsive noise

Content Provider	IEEE Xplore Digital Library
Author	Gandhi, P.P. Kassam, S.A.
Copyright Year	1988
Description	Author affiliation: Moore Sch. of Electr. Eng., Pennsylvania Univ., Philadelphia, PA, USA (Gandhi, P.P.; Kassam, S.A.)
Abstract	The combination filter or C-filter incorporates both temporal and rank-order information of the input sequence to produce its outputs. It can be used as an equalization filter to restore an original waveform which has been dispersed by a channel, in the presence of additive white Gaussian and impulsive noise. The filter coefficients can be designed by using the LMS (least mean squares) algorithm. This approach is examined and an example is given demonstrating the usefulness of the C-filter. The authors consider a technique which combines both rank-order statistics and FIR (finite impulse response) filtering schemes to deconvolve noisy channel characteristics, and to simultaneously reject the impulse noise. The problem of gain normalization at frequencies other than zero is also addressed; this arises during filtering of nonstationary signals in impulsive noise. It is concluded that the C-filter provides a useful alternative to the FIR filter for applications in inverse filtering and frequency-selective nonstationary signal restoration when the additive noise contains impulsive components and step nonstationarity in signal level.<>
Starting Page	1507
Ending Page	1510
File Size	228832
Page Count	4
File Format	PDF
DOI	10.1109/ISCAS.1988.15216
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1988-06-07
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deconvolution Finite impulse response filter Signal restoration Additive noise Frequency Information filtering Information filters Additive white noise Gaussian noise Algorithm design and analysis
Content Type	Text
Resource Type	Article

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