📄 d_arma.m
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%D_ARMA HOSA Demo: Linear Processes - Parametric (ARMA) model estimation
% Blind deconvolution.
echo off
% Demo of armaqs, armarts
% A. Swami Jan 20, 1995
% Copyright (c) 1991-2001 by United Signals & Systems, Inc.
% $Revision: 1.5 $
% RESTRICTED RIGHTS LEGEND
% Use, duplication, or disclosure by the Government is subject to
% restrictions as set forth in subparagraph (c) (1) (ii) of the
% Rights in Technical Data and Computer Software clause of DFARS
% 252.227-7013.
% Manufacturer: United Signals & Systems, Inc., P.O. Box 2374,
% Culver City, California 90231.
%
% This material may be reproduced by or for the U.S. Government pursuant
% to the copyright license under the clause at DFARS 252.227-7013.
clear, clc,
echo on
% ARMA parameter estimation methods
%
% HOSA offers routines, ARMAQS and ARMARTS to estimate
% the parameters of ARMA models.
%
% The test synthetic "z" is an ARMA(2,1) synthetic with AR parameters
% [1,-0.8,0.65] and MA parameters [1,-2]; input was i.i.d. and exponentially
% distributed; white Gaussian noise was added to obtain a SNR of 20 dB.
%
% We will use ARMAQS to estimate the parameters:
% This implements the `q-slice' method which obtains simultaneous
% estimates of the AR and MA parameters.
% We will use p=2, q=1 (true model orders), and 3-rd order cumulants.
%Hit any key to continue
pause
load arma1
[avec,bvec] = armaqs(y,2,1,3,10,128);
% AR estimate:
disp(avec'),
% MA estimate:
disp(bvec')
% Hit any key to continue
pause
% Next, we will use ARMARTS (residual time-series method)
% This algorithm uses a three step procedure; it uses ARRCEST to esimate the
% AR parameters; next, it deconvolves the AR part, and then uses MAEST to
% estimate the MA parameters.
% We will use p=2, q=1 (true model orders), and 3-rd order cumulants.
%Hit any key to continue
pause
[avec,bvec] = armarts(y,2,1,3,12,128);
% AR estimate:
disp(avec'),
% MA estimate:
disp(bvec')
% Hit any key to continue
pause
echo off
clc
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