In array processing, sparse arrays are capable of resolving O (N 2) uncorrelated sources
with N sensors. Sparse arrays have this property because they possess uniform linear array …

Distorted sensors could occur randomly and may lead to the breakdown of a sensor array
system. In this article, we consider an array model within which a small number of sensors …

Sparse linear arrays, such as co-prime and nested arrays, can identify up O (M 2)
uncorrelated sources with only O (M) sensors by utilizing their difference coarray model. In …

Sparse arrays can identify O (N 2) uncorrelated sources using N physical sensors. This
property is because the difference coarray, defined as the differences between sensor …

Sparse linear arrays, such as nested and co-prime arrays, are capable of resolving O (M2)
sources using only O (M) sensors by exploiting their so-called difference coarray model. One …

Sparse arrays have drawn attention because they can identify O (N 2) uncorrelated source
directions using N physical sensors, whereas uniform linear arrays (ULA) find at most N− 1 …

Sparse arrays can resolve more source direction-of-arrivals (DOAs) than sensors in array
processing. This property is achieved with DOA estimators based on the difference coarray …

In this paper, we address the problem of direction of arrival (DOA) estimation for multiple
targets in the presence of sensor failures in a sparse array. Generally, sparse arrays are …

Array signal processing focuses on an array of sensors receiving the incoming waveforms in
the environment, from which source information, such as directions of arrival (DOA), signal …

Recently one-bit quantization has been widely used in field of signal processing because of
its advantages of low cost, low power consumption and high sampling rate. This paper …