基于广义双帕累托先验的离格源欠定DOA估计
Underdetermined DOA Estimation of Off-Grid Sources Based on the Generalized Double Pareto Prior
AI总结：
针对欠定场景下DOA估计的网格失配等问题，提出基于GDP先验的GDPOGSBL方法，结合泰勒展开建模与网格细化，提升了粗网格、少快拍条件下的估计性能。
摘要：
本文研究了一种新型的基于广义双帕累托的离格稀疏贝叶斯学习（GDPOGSBL）方法，以提升欠定场景下波达方向（DOA）估计的性能。该方法利用广义双帕累托（GDP）先验来增强源信号的稀疏性。首先，我们采用一阶线性泰勒展开对真实阵列流形矩阵建模，并通过贝叶斯推理计算离格误差，缓解了欠定场景下的网格字典失配问题。其次，提出了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，尤其在处理粗网格和少快拍数的情况时效果显著。
英文摘要：
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.
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