Compressed sensing for astrophysical signals.
Paper selected for ICECS’16.
In order to reduce power consumption and limit the amount of data acquired and stored for astrophysical signals, an emerging sampling paradigm called compressed sensing (also known as compressive sensing, compressive sampling, CS) could potentially be an efficient solution. The design of radio receiver architecture based on CS requires knowledge of the sparsity domain of the signal and an appropriate measurement matrix. In this paper, we analyze an astrophysical signal (jovian signal with a bandwidth of 40 MHz) by extracting its relevant information via the Radon Transform. Then, we study its sparsity and we establish its sensing modality as well as the minimum number of measurements required. Experimental results demonstrate that our signal is sparse in the frequency domain with a compressibility level of at least 10%. Using the Non Uniform Sampler (NUS) as receiver architecture, we prove that by taking 1/3 of samples at random we can recover the relevant information.