Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once
compressively acquired, many signals need to be processed using advanced techniques …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

Modern radar systems tend to utilize high bandwidth, which requires high sampling rate, and
in many cases, these systems involve phased array configurations with a large number of …

This paper introduces a simple and very general theory of compressive sensing. In this
theory, the sensing mechanism simply selects sensing vectors independently at random …

These notes give a mathematical introduction to compressive sensing focusing on recovery
using1-minimization and structured random matrices. An emphasis is put on techniques for …

Compressive sensing is a new type of sampling theory, which predicts that sparse signals
and images can be reconstructed from what was previously believed to be incomplete …

We present a new bound for suprema of a special type of chaos process indexed by a set of
matrices, which is based on a chaining method. As applications we show significantly …

In the case of multicomponent signals with amplitude and frequency modulations, the
idealized representation which consists of weighted trajectories on the time-frequency (TF) …

Identification of time-varying linear systems, which introduce both time-shifts (delays) and
frequency-shifts (Doppler-shifts), is a central task in many engineering applications. This …

In this paper we solve an open problem concerning the characterization of those
measurable sets Ω⊂ R 2 d that, among all sets having a prescribed Lebesgue measure, can …