www.gusucode.com > signal 工具箱matlab源码程序 > signal/pyulear.m
function varargout = pyulear(x,p,varargin)
%PYULEAR Power Spectral Density (PSD) estimate via Yule-Walker's method.
% Pxx = PYULEAR(X,ORDER) returns the PSD estimate, Pxx, of the
% discrete-time signal X. When X is a vector, it is converted to a
% column vector and treated as a single channel. When X is a matrix, the
% PSD is computed independently for each column and stored in the
% corresponding column of Pxx. Pxx is the distribution of power per unit
% frequency. The frequency is expressed in units of radians/sample.
% ORDER is the order of the autoregressive (AR) model used to produce the
% PSD.
%
% For real signals, PYULEAR returns the one-sided PSD by default; for
% complex signals, it returns the two-sided PSD. Note that a one-sided
% PSD contains the total power of the input signal.
%
% Pxx = PYULEAR(X,ORDER,NFFT) specifies the FFT length used to calculate
% the PSD estimates. For real X, Pxx has (NFFT/2+1) rows if NFFT is
% even, and (NFFT+1)/2 rows if NFFT is odd. For complex X, Pxx always
% has NFFT rows. If NFFT is specified as empty, NFFT is set to either
% 256 or the next power of two greater than the length X, whichever is
% larger.
%
% [Pxx,W] = PYULEAR(X,ORDER,NFFT) returns the vector of normalized
% angular frequencies, W, at which the PSD is estimated. W has units of
% radians/sample. For real signals, W spans the interval [0,Pi] when
% NFFT is even and [0,Pi) when NFFT is odd. For complex signals, W
% always spans the interval [0,2*Pi).
%
% [Pxx,W] = PYULEAR(X,ORDER,W) computes the two-sided PSD at the
% normalized angular frequencies contained in vector W. W must have
% at least two elements.
%
% [Pxx,F] = PYULEAR(X,ORDER,NFFT,Fs) returns a PSD computed as a function
% of physical frequency. Fs is the sampling frequency specified in
% hertz. If Fs is empty, it defaults to 1 Hz.
%
% F is the vector of frequencies (in hertz) at which the PSD is
% estimated. For real signals, F spans the interval [0,Fs/2] when NFFT
% is even and [0,Fs/2) when NFFT is odd. For complex signals, F always
% spans the interval [0,Fs).
%
% [Pxx,F] = PYULEAR(X,ORDER,F,Fs) computes the two-sided PSD at the
% frequencies contained in vector F. F must contain at least two
% elements and be expressed in hertz.
%
% [...] = PYULEAR(...,FREQRANGE) returns the PSD over the specified
% range of frequencies based upon the value of FREQRANGE:
%
% 'onesided' - returns the one-sided PSD of a real input signal X.
% If NFFT is even, Pxx has length NFFT/2+1 and is computed over the
% interval [0,pi]. If NFFT is odd, Pxx has length (NFFT+1)/2 and
% is computed over the interval [0,pi). When Fs is specified, the
% intervals become [0,Fs/2) and [0,Fs/2] for even and odd NFFT,
% respectively.
%
% 'twosided' - returns the two-sided PSD for either real or complex
% input X. Pxx has length NFFT and is computed over the interval
% [0,2*Pi). When Fs is specified, the interval becomes [0,Fs).
%
% 'centered' - returns the centered two-sided PSD for either real or
% complex X. Pxx has length NFFT and is computed over the interval
% (-pi, pi] for even length NFFT and (-pi, pi) for odd length NFFT.
% When Fs is specified, the intervals become (-Fs/2, Fs/2] and
% (-Fs/2, Fs/2) for even and odd length NFFT, respectively.
%
% FREQRANGE may be placed in any position in the input argument list
% after ORDER. The default value of FREQRANGE is 'onesided' when X
% is real and 'twosided' when X is complex.
%
% [Pxx,F,Pxxc] = PYULEAR(...,'ConfidenceLevel',P) returns the P*100%
% confidence interval for Pxx, where P is a scalar between 0 and 1. The
% default value for P is .95. Confidence intervals are computed using a
% Gaussian PDF. Pxxc has twice as many columns as Pxx. Odd-numbered
% columns contain the lower bounds of the confidence intervals;
% even-numbered columns contain the upper bounds. Thus, Pxxc(M,2*N-1) is
% the lower bound and Pxxc(M,2*N) is the upper bound corresponding to the
% estimate Pxx(M,N).
%
% PYULEAR(...) with no output arguments plots the PSD estimate (in decibels
% per unit frequency) in the current figure window.
%
% EXAMPLE:
% x = randn(100,1);
% y = filter(1,[1 1/2 1/3 1/4 1/5],x);
% pyulear(y,4,[],1000); % Uses the default NFFT of 256.
%
% See also PBURG, PCOV, PMCOV, PMTM, PMUSIC, PWELCH, PERIODOGRAM, PEIG,
% ARYULE, PRONY.
% Copyright 1988-2014 The MathWorks, Inc.
narginchk(2,8)
% Precision rules for {x,p} and {NFFT,F,Fs}are enforced inside ARYULE and
% ARSPECTRA respectively
method = @aryule;
[Pxx,freq,msg,units,~,options,msgobj] = arspectra(method,x,p,varargin{:});
if ~isempty(msg), error(msgobj); end
% compute confidence intervals if needed.
if ~strcmp(options.conflevel,'omitted')
% arconfinterval enforces double precision arithmetic internally
Pxxc = arconfinterval(options.conflevel, p, Pxx, x);
elseif nargout>2
Pxxc = arconfinterval(0.95, p, Pxx, x);
else
Pxxc = [];
end
if nargout==0,
% If no output arguments are specified plot the PSD.
freq = {freq};
if strcmpi(units,'Hz'), freq = [freq {'Fs',options.Fs}]; end
hpsd = dspdata.psd(Pxx,freq{:},'SpectrumType',options.range);
% Create a spectrum object to store in the PSD object's metadata.
hspec = spectrum.yulear(p);
hpsd.Metadata.setsourcespectrum(hspec);
% plot the confidence levels if conflevel is specified.
if ~isempty(Pxxc)
hpsd.ConfLevel = options.conflevel;
hpsd.ConfInterval = Pxxc;
end
% center dc if needed
if options.centerdc
centerdc(hpsd);
end
plot(hpsd);
else
% center dc if needed
if options.centerdc
[Pxx, freq, Pxxc] = psdcenterdc(Pxx, freq, Pxxc, options);
end
% If the input is a vector and a row frequency vector was specified,
% return output as a row vector for backwards compatibility.
if size(options.nfft,2)>1 && isvector(x)
Pxx = Pxx.';
end
% Cast to enforce precision rules. Cast frequency only if outputs have
% been requested, otherwise plot using double frequency vectors.
if isa(Pxx,'single')
freq = single(freq);
end
% Assign output arguments.
varargout = {Pxx,freq,Pxxc}; % Pxx=PSD, Sxx=PS
end
% [EOF] pyulear.m