Segmented compressed sampling for analog-to-information conversion: Method and performance analysis

A new segmented compressed sampling (CS) method for analog-to-information conversion (AIC) is proposed. An analog signal measured by a number of parallel branches of mixers and integrators (BMIs), each characterized by a specific random sampling waveform, is first segmented in time into segments. Then the subsamples collected on different segments and different BMIs are reused so that a larger number of samples (at most 2) than the number of BMIs is collected. This technique is shown to be equivalent to extending the measurement matrix, which consists of the BMI sampling waveforms, by adding new rows without actually increasing the number of BMIs. We prove that the extended measurement matrix satisfies the restricted isometry property with overwhelming probability if the original measurement matrix of BMI sampling waveforms satisfies it. We also prove that the signal recovery performance can be improved if our segmented CS-based AIC is used for sampling instead of the conventional AIC ...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Omid Taheri, Sergiy A. Vorobyov
• Keywords: CORR 2010, Education, Measurement Matrix, Sampling Waveforms, Signal Recovery Performance