The word robust has been used in many contexts in signal processing. Our treatment
concerns statistical robustness, which deals with deviations from the distributional …

Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many
applications, robust estimators are needed. Among the scenarios in which robust …

Understand the benefits of robust statistics for signal processing with this authoritative yet
accessible text. The first ever book on the subject, it provides a comprehensive overview of …

Standard-setting, groundbreaking, authoritative, comprehensive—these often overused
words perfectly describe The Circuits and Filters Handbook, Third Edition. This standard …

Recent results in compressed sensing show that a sparse or compressible signal can be
reconstructed from a few incoherent measurements. Since noise is always present in …

Culled from the pages of CRC's highly successful, best-selling The Circuits and Filters
Handbook, Second Edition, Passive, Active, and Digital Filters presents a sharply focused …

A unified and systematic theoretical framework for solving problems related to finite impulse
response (FIR) estimate Optimal and Robust State Estimation: Finite Impulse Response …

In cognitive radio (CR) systems, reliable spectrum sensing techniques are required in order
to avoid interference to the primary users of the spectrum. Whereas most of the existing …

A common problem is that of describing the probability distribution of a single, continuous
variable. A few distributions, such as the normal and exponential, were discovered in the …

In this paper we analyze design aspects of a single-carrier digital communications receiver
in the presence of impulsive noise. We use the additive white symmetric α-stable noise …