3 papers
eess.SP2025
A Comparative Study of Invariance-Aware Loss Functions for Deep Learning-based Gridless Direction-of-Arrival Estimation
Kuan-Lin Chen, Bhaskar D. Rao
Covariance matrix reconstruction has been the most widely used guiding objective in gridless direction-of-arrival (DoA) estimation for sparse linear arrays. Many semidefinite progr…
eess.SP2025
Adaptive and Self-Tuning SBL with Total Variation Priors for Block-Sparse Signal Recovery
Hamza Djelouat, Reijo Leinonen, Mikko J. Sillanpää +2
This letter addresses the problem of estimating block sparse signal with unknown group partitions in a multiple measurement vector (MMV) setup. We propose a Bayesian framework by a…
eess.SP2025
Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology
Kuan-Lin Chen, Bhaskar D. Rao
Localizing more sources than sensors with a sparse linear array (SLA) has long relied on minimizing a distance between two covariance matrices and recent algorithms often utilize s…