  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.
