We study the problem of completing the virtual array of a nested array with a single
snapshot. This involves synthesizing a virtual uniform linear array (ULA) with the same …

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 …

We proposea new framework for leveraging the degrees of freedom in the difference set of a
nested array with limited snapshots. Typically, this difference set (or coarray) is realized by …

It is well‐known that suitably designed sparse arrays (with large and contiguous difference
co‐arrays) are able to localize more sources than sensors in the passive setting, by …

The beamspace transformation (BT) is utilized to convert a uniform circular array (UCA-BT)
to a virtual uniform linear array (VULA) for spatial smoothing and direction-of-arrival (DOA) …

Multiplicative processors combine the beamformed outputs of undersampled subarrays to
estimate the spatial power spectrum. While the multiplicative processor requires fewer …

Using the stochastic Cramer-Rao bound for direction estimation we study the performance of
nonuniformly spaced linear antenna arrays. We show that the estimation accuracy of such …

High dimensional inverse problems are at the heart of numerous modern signal processing
and machine learning applications, where the goal is to sense the physical environment and …

We present a new direction-of-arrival (DOA) estimator for nested arrays, which does not
require source number knowledge and works in the presence of nonuniform noise. We first …

Two dimensional (2-D) direction of arrival (DOA) estimation is a fundamental problem in
array signal processing with wide range of applications. In this paper, an approach to 2-D …