Maximum likelihood-based gridless doa estimation using structured covariance matrix recovery and sbl with grid refinement
We consider the parametric measurement model employed in applications such as line
spectral or direction-of-arrival estimation with the goal to estimate the underlying parameter …
spectral or direction-of-arrival estimation with the goal to estimate the underlying parameter …
Block-sparse signal recovery via general total variation regularized sparse Bayesian learning
One of the main challenges in block-sparse signal recovery, as encountered in, eg, multi-
antenna mmWave channel models, is block-patterned estimation without knowledge of …
antenna mmWave channel models, is block-patterned estimation without knowledge of …
A robust and statistically efficient maximum-likelihood method for DOA estimation using sparse linear arrays
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 …
uncorrelated sources than sensors by using a proper sparse linear array (SLA) at the cost of …
[HTML][HTML] Gridless sparse covariance-based beamforming via alternating projections including co-prime arrays
Y Park, P Gerstoft - The Journal of the Acoustical Society of America, 2022 - pubs.aip.org
This paper presents gridless sparse processing for direction-of-arrival (DOA) estimation. The
method solves a gridless version of sparse covariance-based estimation using alternating …
method solves a gridless version of sparse covariance-based estimation using alternating …
Frequency-domain convolutive bounded component analysis algorithm for the blind separation of dependent sources
X Luo, Z Zhang, T Gong - IEEE Transactions on Instrumentation …, 2023 - ieeexplore.ieee.org
Aiming at the problem of dependent source separation in complex mechanical systems, the
highly universal frequency-domain convolutive bounded component analysis (FDCBCA) …
highly universal frequency-domain convolutive bounded component analysis (FDCBCA) …
Robust and sparse m-estimation of doa
A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows
a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order …
a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order …
Sparse signal recovery and source localization via covariance learning
E Ollila - arXiv preprint arXiv:2401.13975, 2024 - arxiv.org
In the Multiple Measurements Vector (MMV) model, measurement vectors are connected to
unknown, jointly sparse signal vectors through a linear regression model employing a single …
unknown, jointly sparse signal vectors through a linear regression model employing a single …
[HTML][HTML] Robust and sparse M-estimation of DOA
A robust and sparse Direction of Arrival (DOA) estimator is derived for array data that follows
a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order …
a Complex Elliptically Symmetric (CES) distribution with zero-mean and finite second-order …
[HTML][HTML] Empirical Bayesian localization of event-related time-frequency neural activity dynamics
Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations
across human brain regions is an important problem in functional brain imaging and human …
across human brain regions is an important problem in functional brain imaging and human …
General total variation regularized sparse Bayesian learning for robust block-sparse signal recovery
Block-sparse signal recovery without knowledge of block sizes and boundaries, such as
those encountered in multi-antenna mmWave channel models, is a hard problem for …
those encountered in multi-antenna mmWave channel models, is a hard problem for …