No BSD License
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Px=overlay(N,omega,A,sigma,nu...
OVERLAY Forms multiple power spectral density estimates.
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R=covar(x,p)
COVAR Generates a covariance matrix/
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[a,epsilon]=rtoa(r)
RTOA Levinson-Durbin recursion.
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a=gtoa(gamma)
GTOA Step-up recursion
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acm(x,p)
ACM Find an all-pole model using the autocorrelation method
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bart(x,nsect)
BART Bartlett's method of periodogram averaging.
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bt_pc(x,p,M)
BT_PC Frequency estimation using principal components Blackman-Tukey.
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burg(x,p)
BURG All-pole modeling using the Burg algorithm.
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convm(x,p)
CONVM Generates a convolution matrix
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covm(x,p)
COVM Find an all-pole model using the covariance method
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durbin(x,p,q)
DURBIN Find a moving average model using Durbin's method
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ev(x,p,M)
EV Frequency estimation using the eigenvector method
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fcov(x,p)
FCOV Forward covariance algorithm.
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gamma=atog(a)
ATOG Step-down recursion
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ipf(x,p,q,n,a)
IPF Pole-zero signal modeling using iterative prefiltering.
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lms(x,d,mu,nord,a0)
LMS Adaptive filtering using the Widrow-Hoff LMS algorithm.
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mcov(x,p)
MCOV All-pole signal modeling using the modified covariance method.
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mem(x,p)
MEM Spectrum estimation using the Maximum Entropy Method (MEM).
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min_norm(x,p,M)
MIN_NORM Frequency estimation using the minimum norm algorithm.
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minvar(x,p)
MINVAR Spectrum estimation using the minimum variance method.
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mper(x,win,n1,n2)
MPER Spectrum estimation using the modified periodogram.
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music(x,p,M)
MUSIC Frequency estimation using the MUSIC algorithm.
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nlms(x,d,beta,nord,a0)
NLMS Normalized LMS adaptive filtering algorithm.
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pade(x,p,q)
PADE Model a signal using the Pade approximation method
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per(x,n1,n2)
PER Estimate the spectrum of a process using the periodogram
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phd(x,p)
PHD Frequency estimation using the Pisarenko harmonic decomposition.
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prony(x,p,q)
PRONY Model a signal using Prony's method
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r=ator(a,b)
ATOR Inverse Levinson-Durbin recursion.
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r=gtor(gamma,epsilon)
GTOR Inverse Levinson-Durbin recursion.
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rls(x,d,nord,lambda)
RLS Recursive Least Squares.
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rtog(r)
RTOG Levinson-Durbin recursion.
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shanks(x,p,q)
SHANKS Model a signal using Shanks' method
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sper(x,win,M,n1,n2)
SPER Spectrum estimation using periodogram smoothing.
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spike(g,n0,n)
SPIKE Finds the FIR least squares inverse of g(n)
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welch(x,L,over,win)
WELCH Spectrum estimation using Welch's method.
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x=glev(r,b)
GLEV Levinson recursion.
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View all files
Statistical Digital Signal Processing and Modeling
by Monson Hayes
20 Aug 2002
(Updated 21 Nov 2002)
statistical digital signal processing, DSP, digital filtering, signal modeling
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| File Information |
| Description |
Suitable for the senior/graduate level in advanced DSP or digital filtering, this text focuses on signal modeling.
For a full book description and ordering information, please refer to http://www.mathworks.com/support/books/book1367.jsp. |
| MATLAB release |
MATLAB 5.2 (R10)
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