SparSig Sparse Signal Processing in Wireless Communication

An article about Spectral Compressive Sensing with Polar Interpolation

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Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.

Paper published at International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013.


Computation Time

In the article, the computation time for each algorithm is shown in a table. A more detailed visualization is in the following figures.

Fig1. - Noise-less experiment


Fig2. - Noisy experiment