www.gusucode.com > 步态识别代码视频序列ppt展示及相关文献matlab源码程序 > lslp/example.m
% % % Comparison of Least-Squares Linear Prediction Maximum Entroy Spectra % and Spectra produced by FFTs. % % J. E. Boyd - 15-Jul-97 % % % % first example is taken from Barrodale and Erickson's paper % % load signal0.dat; signal0 = signal0'; % use forback to get the coefficients for forward and backward prediction % - compare the values to B&E's paper disp( 'Prediction coefficients for Barrodale and Erickson''s example' ); a = forback( signal0, 4 ) % use comp spectrum to compute the spectrum from the coefficents [f, pwr1] = compspect( a, 1000 ); % compute the spectrum from an FFT [f, pwr2] = fftspect( signal0, 1000 ); % scale the two power spectra and compare in a plot pwr1 = pwr1 / max( pwr1 ); pwr2 = pwr2 / max( pwr2 ); disp( 'Plotting example 1 ...' ); plot( f, [pwr1, pwr2] ); title( 'Barrodale and Erickson Example' ); disp( 'note that the peak in the FFT spectrum isn''t even' ); disp( ' at the correct frequency' ); % % % second example consists of three samples of a sinusoid % % load signal1.dat; signal1 = signal1'; % first the linear prediction method [f, pwr1] = compspect( forback( signal1, 2 ), 1000 ); % now the FFT version [f, pwr2] = fftspect( signal1, 1000 ); % scale and plot them pwr1 = pwr1 / max( pwr1 ); pwr2 = pwr2 / max( pwr2 ); figure; disp( 'Plotting example 2 ...' ); plot( f, [pwr1, pwr2] ); title( 'Short (5-sample) Sinusoid Series' ); disp( 'Note the resolution that the linear prediction method yields' ); % % % now try an example from the gait study, y_c for one sequence % % % read the signal and remove the background load signal2.dat signal2 = subbackground( signal2' ); % first the linear prediction method [f, pwr1] = compspect( forback( signal2, 20 ), 1000 ); % now the FFT version [f, pwr2] = fftspect( signal2, 1000 ); % scale and plot them pwr1 = pwr1 / max( pwr1 ); pwr2 = pwr2 / max( pwr2 ); figure; disp( 'Ploting example 3 ...' ); plot( f, [pwr1, pwr2] ); title( 'Power Spectra for Example y_c Series' ); disp( 'Note the side-lobes everywhere in the FFT spectrum' );