remez: Parks-McClellan optimal FIR filter design

In signal: Signal Processing

Description

Parks-McClellan optimal FIR filter design.

Usage

remez(n, f, a, w = rep(1.0, length(f) / 2),
      ftype = c('bandpass', 'differentiator', 'hilbert'),
      density = 16)

Arguments

n	order of the filter (1 less than the length of the filter)
f	frequency at the band edges in the range (0, 1), with 1 being the Nyquist frequency.
a	amplitude at the band edges.
w	weighting applied to each band.
ftype	options are: 'bandpass', 'differentiator', and 'hilbert'.
density	determines how accurately the filter will be constructed. The minimum value is 16, but higher numbers are slower to compute.

Value

The FIR filter coefficients, an array of length(n+1), of class Ma.

Author(s)

Original Octave version by Paul Kienzle. Conversion to R by Tom Short. It uses C routines developed by Jake Janovetz.

References

Rabiner, L. R., McClellan, J. H., and Parks, T. W., “FIR Digital Filter Design Techniques Using Weighted Chebyshev Approximations”, IEEE Proceedings, vol. 63, pp. 595 - 610, 1975.

https://en.wikipedia.org/wiki/Fir_filter

Octave Forge https://octave.sourceforge.io/

See Also

filter, Ma, fftfilt, fir1

Examples

f1 <- remez(15, c(0, 0.3, 0.4, 1), c(1, 1, 0, 0))
freqz(f1)

Example output

Attaching package: 'signal'

The following objects are masked from 'package:stats':

    filter, poly

signal documentation built on May 25, 2021, 3 p.m.