Conventional canonical polyadic decomposition (CPD) approach for tensor-based sparse
array direction-of-arrival (DOA) estimation typically partitions the coarray statistics to …

Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed
to handle multidimensional sub-Nyquist sampled signals. Furthermore, augmented virtual …

Augmented coarrays can be derived from spatially undersampled signals of sparse arrays
for underdetermined direction-of-arrival (DOA) estimation. With the extended dimension of …

This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA)
estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs …

A new tensor-based approach, which is capable of significantly increasing the degrees of
freedom (DOF) of an L-shaped array consisting of two orthogonal uniform linear arrays …

The coprime array owns a sparse array structure, which can effectively alleviate mutual
coupling, while the holes existing in the difference co-array (DCA) greatly reduce the …

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of
arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of …

As a generalized coprime array structure, the coprime array with displaced subarrays
(CADiS) allows a large minimum inter-element spacing by introducing a specific …

This letter aims to estimate the 2-D direction-of-arrival (DOA) using a polarized uniform
rectangular array (URA) under multipath propagation. To leverage the tensorial nature, a …

A coprime array is capable of achieving more degrees-of-freedom for direction-of-arrival
(DOA) estimation than a uniform linear array when utilizing the same number of sensors …