Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been
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Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been quantified. We aim to fill the gap by computing the performance of diverse compressive data acquisition schemes by the output signal-to-noise ratio (SNR) they provide with the same input signal. The SNR is assessed analytically, and also confirmed numerically with simulated data. Only with a scheme of compressive data acquisition starting directly at reception (with no receiver noise yet), CS is less complicated and still performs as good as, if not better than, existing sensing.@en
