Flat-Top Windowing Function for the Accurate Measurement of a Sinusoid's Peak Amplitude Based on FFT Data

Rick Lyons December 14, 2011 Coded in Matlab

The following Matlab code provides an accurate method of estimating time-domain sinewave peak amplitudes based on the fast Fourier transform (FFT) data. Such an operation sounds simple, but the scalloping loss characteristic of FFTs complicates the process. The code given here implements 'flat-top' windowing by way of frequency-domain convolution in a way that greatly reduce scalloping-loss amplitude-estimation problems.

To test this function, you can use the snippet here.

For more information, see Rick Lyon's blog post titled: Accurate Measurement of a Sinusoid's Peak Amplitude Based on FFT Data

 

function [Windowed_Spec] = Wind_Flattop(Spec)

%  Given an input spectral sequence 'Spec', that is the 
%  FFT of some time sequence 'x', Wind_Flattop(Spec) 
%  returns a spectral sequence that is equivalent
%  to the FFT of a flat-top windowed version of time 
%  sequence 'x'.  The peak magnitude values of output 
%  sequence 'Windowed_Spec' can be used to accurately 
%  estimate the peak amplitudes of sinusoidal components 
%  in time sequence 'x'.

%   Input: 'Spec' (a sequence of complex FFT sample values)
%
%   Output: 'Windowed_Spec' (a sequence of complex flat-top  
%                            windowed FFT sample values)
%
%   Based on Lyons': "Reducing FFT Scalloping Loss Errors 
%   Without Multiplication", IEEE Signal Processing Magazine, 
%   DSP Tips & Tricks column, March, 2011, pp. 112-116.
%
%   Richard Lyons [December, 2011]

N = length(Spec);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%   Perform freq-domain convolution
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
g_Coeffs = [1, -0.94247, 0.44247];

% Compute first two convolved spec samples using spectral 'wrap-around'
Windowed_Spec(1) = g_Coeffs(3)*Spec(N-1) ...
    +g_Coeffs(2)*Spec(N) + Spec(1) ...
    +g_Coeffs(2)*Spec(2) + g_Coeffs(3)*Spec(3);

Windowed_Spec(2) = g_Coeffs(3)*Spec(N) ...
    +g_Coeffs(2)*Spec(1) + Spec(2) ...
    +g_Coeffs(2)*Spec(3) + g_Coeffs(3)*Spec(4);

% Compute last two convolved spec samples using spectral 'wrap-around'
Windowed_Spec(N-1) = g_Coeffs(3)*Spec(N-3) ...
    +g_Coeffs(2)*Spec(N-2) + Spec(N-1) ...
    +g_Coeffs(2)*Spec(N) + g_Coeffs(3)*Spec(1);

Windowed_Spec(N) = g_Coeffs(3)*Spec(N-2) ...
    +g_Coeffs(2)*Spec(N-1) + Spec(N) ...
    +g_Coeffs(2)*Spec(1) + g_Coeffs(3)*Spec(2);

% Compute convolved spec samples for the middle of the spectrum
for K = 3:N-2
	Windowed_Spec(K) = g_Coeffs(3)*Spec(K-2) ...
        +g_Coeffs(2)*Spec(K-1) + Spec(K) ...
        +g_Coeffs(2)*Spec(K+1) + g_Coeffs(3)*Spec(K+2);
end % % End of 'Wind_Flattop(Spec)' function

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