There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …

The Vandermonde decomposition of Toeplitz matrices, discovered by Carathéodory and
Fejér in the 1910s and rediscovered by Pisarenko in the 1970s, forms the basis of modern …

In this paper we offer a review and bibliography of work on Hankel low-rank approximation
and completion, with particular emphasis on how this methodology can be used for time …

Signals are generally modeled as a superposition of exponential functions in spectroscopy
of chemistry, biology, and medical imaging. For fast data acquisition or other inevitable …

Many signals are modeled as a superposition of exponential functions in spectroscopy of
chemistry, biology, and medical imaging. This paper studies the problem of recovering …

It is a classic problem to estimate continuous-time sparse signals, like point sources in a
direction-of-arrival problem, or pulses in a time-of-flight measurement. The earliest …

Maximum likelihood estimation (MLE) provides a well-known benchmark for line spectral
estimation and has been extensively studied in the parameter domain using a variety of …

This paper addresses the problem of direction-of-arrival (DOA) estimation for constant
modulus (CM) source signals using a uniform or sparse linear array. Existing methods …

Low rank approximation is an important tool in many applications. Given an observed matrix
with elements corrupted by Gaussian noise it is possible to find the best approximating …

This paper is concerned with the problem of line spectral estimation. Reweighted atomic
norm minimization based on Toeplitz model (RAM-T) is a promising approach that promotes …