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.

翻译：本文研究一种新的基于离网格稀疏贝叶斯学习的广义双帕累托（GDPOGSBL）方法，以提升欠定场景下波达方向（DOA）估计的性能。该方法通过利用广义双帕累托（GDP）先验增强源信号的稀疏性。首先，采用一阶线性泰勒展开对真实阵列流型矩阵进行建模，并利用贝叶斯推断计算离网格误差，从而缓解欠定场景中的网格字典失配问题。其次，引入一种创新的网格细化方法，将网格点作为迭代参数以最小化源与网格点之间的建模误差。数值仿真结果验证了所提策略的优越性，尤其是在处理粗网格和少量快拍的场景中。