Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing - implementation -

I first spotted this on the LinkedIn Calibration Club (the name of the group comes from the inability for most people to talk about calibration issues and the similarity with the first two rules of Fight Club). Here it is, the calibration of a compressive senisng system: 

Model-Based Calibration of Filter Imperfections in the Random Demodulator for Compressive Sensing by Pawel Jerzy Pankiewicz, Thomas Arildsen, Torben Larsen. The abstract reads:

The random demodulator is a recent compressive sensing architecture providing efficient sub-Nyquist sampling of sparse band-limited signals. The compressive sensing paradigm requires an accurate model of the analog front-end to enable correct signal reconstruction in the digital domain. In practice, hardware devices such as filters deviate from their desired design behavior due to component variations. Existing reconstruction algorithms are sensitive to such deviations, which fall into the more general category of measurement matrix perturbations. This paper proposes a model-based technique that aims to calibrate filter model mismatches to facilitate improved signal reconstruction quality. The mismatch is considered to be an additive error in the discretized impulse response. We identify the error by sampling a known calibrating signal, enabling least-squares estimation of the impulse response error. The error estimate and the known system model are used to calibrate the measurement matrix. Numerical analysis demonstrates the effectiveness of the calibration method even for highly deviating low-pass filter responses. The proposed method performance is also compared to a state of the art method based on discrete Fourier transform trigonometric interpolation.

Matlab code to reproduce the figures is located at: http://www.sparsesampling.com/mbc/

Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.