It is known that there exist two kinds of methods for direction-of-arrival (DOA) estimation in
the literature: the subspace-based method and the sparsity-based method. However …

A new direction-of-arrival (DOA) estimation method is proposed based on a novel data
model using the concept of a sparse representation of array covariance vectors (SRACV), in …

Unknown mutual coupling effect can degrade the performance of a direction of arrival
estimation method. In this letter, a new method is proposed for uniform linear arrays (ULAs) …

The computationally prohibitive multi-dimensional searching procedure greatly restricts the
application of the maximum likelihood (ML) direction-of-arrival (DOA) estimation method in …

In this letter, we consider the direction-of-arrival (DOA) estimation in the presence of
unknown mutual coupling in application to uniform linear arrays (ULAs). A novel method is …

The low-rank matrix reconstruction (LRMR) approach is widely used in direction-of-arrival
(DOA) estimation. As the rank norm penalty in an LRMR is NP-hard to compute, the nuclear …

As is well known, nonuniform linear arrays have significant advantages in array aperture
and degrees of freedom over uniform linear arrays. Using their difference coarrays …

In this letter, we address the problem of direction finding using coprime array, which is one of
the most preferred sparse array configurations. Motivated by the fact that non-uniform …

Among the existing sparsity-inducing direction-of-arrival (DOA) estimation methods, the
sparse Bayesian learning (SBL) based ones have been demonstrated to achieve enhanced …

In this letter, a deep learning framework for direction of arrival (DOA) estimation is
developed. We first show that the columns of the array covariance matrix can be formulated …