Implementation models for analog-to-information conversion via random sampling

Abstract

We develop a framework for analog-to-information conversion based on the theory of information recovery from random samples. The framework enables sub-Nyquist acquisition and processing of wideband signals that are sparse in a local Fourier representation. We present the random sampling theory associated with an efficient information recovery algorithm to compute the spectrogram of the signal. Additionally, we develop a hardware design for the random sampling system that demonstrates a consistent reconstruction fidelity in the presence of sampling jitter, which forms the main source of non-ideality in a practical system implementation.