2022/04/20 by Ruchi Pandey, Pandey, Ruchi, Santosh Nannuru +1
Computer Science · #Audio and Speech Processing (eess.AS) #Blind Source Separation Techniques #Direction-of-Arrival Estimation Techniques #FOS: Electrical engineering #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.09647
openalex publication_date 2022/04/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Directions of arrival (DOA) estimation or localization of sources is an important problem in many applications for which numerous algorithms have been proposed. Most localization methods use block-level processing that combines multiple data snapshots to estimate DOA within a block. The DOAs are assumed to be constant within the block duration. However, these assumptions are often violated due to source motion. In this paper, we propose a signal model that captures the linear variations in DOA within a block. We applied conventional beamforming (CBF) algorithm to this model to estimate linear DOA trajectories. Further, we formulate the proposed signal model as a block sparse model and subsequently derive sparse Bayesian learning (SBL) algorithm. Our simulation results show that this linear parametric DOA model and corresponding algorithms capture the DOA trajectories for moving sources more accurately than traditional signal models and methods.