Wednesday, May 22, 2013

PalBOMP/PolBOMP: Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation - implementation -

Karsten Fyhn just let me know of their recent article and attendant code repository. 

We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Moreover, we evaluate six algorithms for estimation of parameters in sparse translation-invariant signals, exemplified with the time delay estimation problem. The evaluation is based on three performance metrics: estimator precision, sampling rate and computational complexity. We use compressive sensing with all the algorithms to lower the necessary sampling rate and show that it is still possible to attain good estimation precision and keep the computational complexity low. The proposed algorithms are based on polar interpolation and our numerical experiments show that they outperform existing approaches that either leverage polynomial interpolation or are based on a conversion to an frequency-estimation problem followed by a super-resolution algorithm. The algorithms studied here provide various tradeoffs between computational complexity, estimation precision and necessary sampling rate. The work shows that compressive sensing for the class of sparse translation-invariant signals allows for a lower sampling rate and that the use of polar interpolation increases the estimation precision.

The MATLAB code for the paper is available at

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