  In this letter, we investigate a new generalized double Pareto based on
off-grid sparse Bayesian learning (GDPOGSBL) approach to improve the
performance of direction of arrival (DOA) estimation in underdetermined
scenarios. The method aims to enhance the sparsity of source signal by
utilizing the generalized double Pareto (GDP) prior. Firstly, we employ a
first-order linear Taylor expansion to model the real array manifold matrix,
and Bayesian inference is utilized to calculate the off-grid error, which
mitigates the grid dictionary mismatch problem in underdetermined scenarios.
Secondly, an innovative grid refinement method is introduced, treating grid
points as iterative parameters to minimize the modeling error between the
source and grid points. The numerical simulation results verify the superiority
of the proposed strategy, especially when dealing with a coarse grid and few
snapshots.
