Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array
imperfections and outliers, making it challenging to realize accurate estimates in real …

Sparse Bayesian learning (SBL) has become a popular approach for direction-of-arrival
(DOA) estimation, but its computational complexity for Bayesian inference is quite high …

This paper proposes a Bayesian method to address the farfield narrowband direction-of-
arrival (DOA) estimation problem with time-varying arrays, whose elements relatively move …

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

In Direction‐of‐arrival (DOA) estimation, the real‐valued sparse Bayesian algorithm
degrades the estimation performance by decomposing the complex value into real and …

For direction-of-arrival (DOA) estimation problems, sparse Bayesian learning (SBL) has
achieved excellent estimation performance, especially in sparse arrays. However, numerous …

The recent trend of research on direction-of-arrival estimation is to localize more
uncorrelated sources than sensors by using a proper sparse linear array (SLA) at the cost of …

Abstract Sparsity-based Direction-Of-Arrival (DOA) estimation via l1-norm optimization
requires fine tuning of the regularization parameter and large computational times. To …

In this letter, we address the problem of Direction of Arrival (DOA) estimation with
nonuniform linear array in the context of sparse Bayesian learning (SBL) framework. The …

In this paper, a new method is proposed for direction-of-arrival (DOA) estimation of coherent
signals with improved sparse representation in unknown spatially correlated Gaussian …