Exploiting signal sparseness for reduced-rate sampling

摘要: The rate at which signals are sampled in their native form (e.g. the “time domain” for many of interest) order to capture all information a signal - so-called Nyquist traditional sampling equals one over twice Fourier bandwidth signal. This process exploits knowledge finite Alternatively, if signal's spectrum were available, could be domain, and it known that some coefficients negligible, number samples required reduced. If had such property called sparseness would possible instead sample reduced its while still capturing information? Moreover, do so without knowing exactly negligible? In this paper we examine recently introduced approach compressive (CS) attempts go beyond exploitation bandwidth, exploit allow “under sampled” losing information. We will develop concept CS based on provide justification compressive-sampling process, including an explanation need randomness subsequent reconstruction from samples. addition, examples applications provided, along with simulation results.