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% RGK.m Version 1.0 4 February 1995% The following information is included in the Matlab function rgk.m :%----------------------------------------------------------------------------%% SIGNAL-DEPENDENT TIME-FREQUENCY ANALYSIS USING A RADIAL GAUSSIAN KERNEL %%----------------------------------------------------------------------------%%% This Matlab function implements the ``Optimal Radially Gaussian Kernel% Time-Frequency Representation.'' For details, please consult either% the paper%% R. G. Baraniuk and D. L. Jones, ``Signal-Dependent Time-Frequency% Analysis Using a Radially Gaussian Kernel,'' Signal Processing, % Vol. 32, No. 3, pp. 263-284, June 1993.%% or the thesis%% R. G. Baraniuk, ``Shear Madness: Signal-Dependent and Metaplectic % Time-Frequency Representations,'' Ph.D. Thesis, Department of% Electrical and Computer Engineering, University of Illinois at% Urbana-Champaign, August 1992. Also Coordinated Science Laboratory% Technical Report No. UILU-ENG-92-2226, 1992. See Chapter 6 and% Appendices B and G.%% Equation numbers in the comments below refer to the paper unless% otherwise noted. We have tried to keep as close as possible to the% notation of these documents.%%% FLOW OF THE ALGORITHM:%% Step 1: Compute the rectangularly sampled ambiguity function (AF)% of the signal%% *** Uses the separate function AMBNB ***% (included in this distribution)%% Step 2: Interpolate the AF to polar coordinates%% Step 3: Solve for the optimal kernel spread vector using the% so-called "step-project" algorithm [Eqs. (40)-(42)]%% Step 4: Compute the optimal kernel in polar coordinates%% Step 5: Interpolate the optimal kernel to rectangular coordinates%% Step 6: Inverse FFT the optimal-kernel x AF product to get the% optimal time-frequency representation%%% QUESTIONS? COMMENTS? Drop us a line:%% Paulo Goncalves gpaulo@rice.edu% Richard Baraniuk richb@rice.edu% http://www-dsp.rice.edu%%----------------------------------------------------------------------------%% Help information available in Matlab:%RGK Optimal radially Gaussian kernel time-frequency representation%% Useage: [tfr,Phi,sigma,its] = rgk(s,alpha)%% Input: - s : column or row vector containing the signal to be% analyzed% - alpha : normalized volume of the optimal kernel% reasonable values: 1 < alpha < 5% alpha = 1 => optimal kernel has same volume as a% spectrogram kernel%% Output: - tfr : optimal radially Gaussian time-frequency representation% - Phi : optimal radially Gaussian kernel% - sigma : spread function parametrized by the radial angle in the % ambiguity domain% - its : number of iterations of the step-projection algorithm% to converge to a (local) maximum%% Example: Two parallel linear chirps% t = (0:127);% s1 = hamming(128)' .* cos(0.2*t + 0.008*t.^2);% s2 = hamming(128)' .* cos(0.6*t + 0.008*t.^2);% s = s1 + s2;% tfr = rgk(s,2);% contour(tfr); xlabel('time'); ylabel('frequency')%% See also: AMBNB% Copyright information:%----------------------------------------------------------------------------%%File Name: rgk.m%Last Modification Date: 1/26/96 18:30:22%Current Version: rgk.m 1.2%File Creation Date: Sun Jan 21 16:36:09 1996%Author: Paulo Goncalves <gpaulo@ece.rice.edu>%Extra Verbiage: Richard Baraniuk <richb@rice.edu>%%Copyright: All software, documentation, and related files in this distribution% are Copyright (c) 1996 Rice University%%Permission is granted for use and non-profit distribution providing that this%notice be clearly maintained. The right to distribute any portion for profit%or as part of any commercial product is specifically reserved for the author.%%Change History:%%----------------------------------------------------------------------------%
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