COMPRESSIVE SENSE BASED RECONSTRUCTION ALGORITHM FORNON-UNIFORM SAMPLING BASED DATA CONVERTER

摘要

Compressive sensing is an emerging field that attempts to prevent the losses associated with data compression and improve efficiency overall, and compressive sensing looks to perform the compression before or during capture, before energy is wasted. Here, a reconstruction algorithm is proposed for a compressive sensing successive approximation register (SAR) analog-to-digital converter (ADC). Accordingly, an analog signal is converted to a first digital signal at a sampling frequency that is less than a Nyquist frequency for the analog signal, and a second digital signal is constructed from the first digital signal with a box constrained linear optimization process such that the second digital signal is approximately equal to an analog-to-digital conversion of the analog signal at the Nyquist frequency for the analog signal.