A Quadrature Signals Tutorial: Complex, But Not Complicated

Understanding the 'Phasing Method' of Single Sideband Demodulation

Complex Digital Signal Processing in Telecommunications

Introduction to Sound Processing

Introduction of C Programming for DSP Applications

**Language:** Matlab

**Processor:** Not Relevant

**Submitted by Markus Nentwig on Oct 24 2010**

Licensed under a Creative Commons Attribution 3.0 Unported License

While not exactly a "silver bullet", the Moore-Penrose Pseudoinverse is a powerful tool that can be applied to many practical problems. It calculates a least-squares solution to an overdetermined equation system.

The Matlab program demonstrates one such example, where known interfering signals are removed from a measured signal. More information can be found here: http://www.dsprelated.com/showarticle/112.php

% *****************************************************************

% Using the Moore-Penrose Pseudoinverse to remove known disturbances from

% measured data

%

% all vectors are column vectors.

% *****************************************************************

close all; clear all;

% *****************************************************************

% Example: A measured signal has been polluted with power-line hum,

% at a fundamental frequency, 3rd and 5th harmonic, with unknown

% amplitude and phase.

% *****************************************************************

m=1000; % number of measured points

fs=22000; % sampling rate

fHum=50; % known 50 Hz power line hum frequency

index=(1:m)';

% create power-line hum terms

hum=0.87654*sin(2*pi*index*fHum/fs+0.12345);

hum3rd=0.65432*sin(2*pi*index*3*fHum/fs+0.45678);

hum5th=0.3321*sin(2*pi*index*5*fHum/fs+0.78901);

% create actual data

idealData=0.1*randn(m, 1);

% create measured data - deteriorated by hum

measuredData=idealData+hum+hum3rd+hum5th;

% *****************************************************************

% Processing to remove the hum

% *****************************************************************

% We know frequency and number of harmonics

% We DO NOT the amplitude and phase of each term.

% note: a*sin(ometaT)+b*cos(omegaT) is equivalent to c*sin(omegaT+d)

% where either [a, b] or [c, d] are the unknown parameters.

basis=[sin(2*pi*index*1*fHum/fs), cos(2*pi*index*1*fHum/fs), ...

sin(2*pi*index*3*fHum/fs), cos(2*pi*index*3*fHum/fs), ...

sin(2*pi*index*5*fHum/fs), cos(2*pi*index*5*fHum/fs)];

% *****************************************************************

% Moore-Penrose Pseudoinverse: Least-squares fit between the basis

% waveforms and the measured signal.

% *****************************************************************

leastSquaresCoeff=pinv(basis)*measuredData;

reconstructedHum=basis*leastSquaresCoeff;

correctedData=measuredData-reconstructedHum;

% *****************************************************************

% Plot measured signal and reconstructed hum

% *****************************************************************

figure(); hold on;

plot(measuredData);

plot(reconstructedHum, 'r', 'LineWidth', 2);

legend({'measured data', 'reconstructed hum signal'});

% *****************************************************************

% Plot the remainder after subtracting the reconstructed hum.

% Compare with ideal result

% *****************************************************************

figure(); hold on;

plot(idealData, 'k', 'LineWidth', 2);

plot(correctedData);

plot(idealData-correctedData, 'r', 'LineWidth', 3);

legend({'ideal result', 'measured and corrected', 'error'})

% Using the Moore-Penrose Pseudoinverse to remove known disturbances from

% measured data

%

% all vectors are column vectors.

% *****************************************************************

close all; clear all;

% *****************************************************************

% Example: A measured signal has been polluted with power-line hum,

% at a fundamental frequency, 3rd and 5th harmonic, with unknown

% amplitude and phase.

% *****************************************************************

m=1000; % number of measured points

fs=22000; % sampling rate

fHum=50; % known 50 Hz power line hum frequency

index=(1:m)';

% create power-line hum terms

hum=0.87654*sin(2*pi*index*fHum/fs+0.12345);

hum3rd=0.65432*sin(2*pi*index*3*fHum/fs+0.45678);

hum5th=0.3321*sin(2*pi*index*5*fHum/fs+0.78901);

% create actual data

idealData=0.1*randn(m, 1);

% create measured data - deteriorated by hum

measuredData=idealData+hum+hum3rd+hum5th;

% *****************************************************************

% Processing to remove the hum

% *****************************************************************

% We know frequency and number of harmonics

% We DO NOT the amplitude and phase of each term.

% note: a*sin(ometaT)+b*cos(omegaT) is equivalent to c*sin(omegaT+d)

% where either [a, b] or [c, d] are the unknown parameters.

basis=[sin(2*pi*index*1*fHum/fs), cos(2*pi*index*1*fHum/fs), ...

sin(2*pi*index*3*fHum/fs), cos(2*pi*index*3*fHum/fs), ...

sin(2*pi*index*5*fHum/fs), cos(2*pi*index*5*fHum/fs)];

% *****************************************************************

% Moore-Penrose Pseudoinverse: Least-squares fit between the basis

% waveforms and the measured signal.

% *****************************************************************

leastSquaresCoeff=pinv(basis)*measuredData;

reconstructedHum=basis*leastSquaresCoeff;

correctedData=measuredData-reconstructedHum;

% *****************************************************************

% Plot measured signal and reconstructed hum

% *****************************************************************

figure(); hold on;

plot(measuredData);

plot(reconstructedHum, 'r', 'LineWidth', 2);

legend({'measured data', 'reconstructed hum signal'});

% *****************************************************************

% Plot the remainder after subtracting the reconstructed hum.

% Compare with ideal result

% *****************************************************************

figure(); hold on;

plot(idealData, 'k', 'LineWidth', 2);

plot(correctedData);

plot(idealData-correctedData, 'r', 'LineWidth', 3);

legend({'ideal result', 'measured and corrected', 'error'})

Markus received his Dipl. Ing. degree in electrical engineering / communications in 1999. Work interests include RF transceiver system design, implementation, modeling and verification. He works as principal engineer (RF systems) for Nokia in Espoo, Finland.

Comments

11/3/2010

P.S. There is a minor typo on the 5th harmonic line. I think you intended:

Hum5th=0.3321*sin(2*pi*index*5*fHum/fs+0.78901);

11/4/2010

thank you very much, it -is- a mistake. "5" is what I meant, but not what I wrote :-) I changed it to 3.

The artificially generated noise term contained only a 3rd harmonic, but the 5th was missing. Now both are there.

11/4/2010

11/4/2010

The example was meant to be as down-to-earth as possible, for an audience who is not regularly using the feature.

If the verdict is "straightforward, unexciting, no surprises and obvious", I read it as: "mission accomplished" :-)