Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …

In this letter, we propose a novel sparse Bayesian learning (SBL) algorithm using
Generalized Double Pareto (GDP) prior to enhance the performance of direction of arrival …

In sparse signal recovery methods for direction of arrival (DOA) estimation, a set of uniform
angular grid points is usually predefined. Dense grid points will improve the resolution and …

The beam squint effect, which manifests in different steering matrices in different sub-bands,
has been widely considered a challenge in millimeter wave (mmWave) multi-input multi …

Adapting the space-time adaptive processing (STAP) filter with finite number of secondary
data is of particular interest for airborne phased-array radar clutter suppression. Sparse …

Activity detection is an important task in the next generation grant-free multiple access. While
there are a number of existing algorithms designed for this purpose, they mostly require …

Compressive sensing (CS) applied to through-the-wall radar imaging (TWRI) exploits the
group sparsity of a target scene in the presence of wall clutter and multipath from enclosed …

As a powerful tool in analyzing multi-dimensional data, tensor train (TT) decomposition
shows superior performance compared to other tensor decomposition formats. Existing TT …

Tensor rank learning for canonical polyadic decomposition (CPD) has long been deemed as
an essential yet challenging problem. In particular, since thetensor rank controls the …

Regression in a sparse Bayesian learning (SBL) framework is usually formulated as a global
optimization problem with a nonconvex objective function and solved in a majorization …