Dou Rui, Ding Feilong, Chen Xi, Wang Jian, Yu Deyong, Tang Yuangui
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
J Acoust Soc Am. 2024 Mar 1;155(3):2087-2098. doi: 10.1121/10.0025284.
Many recent works have addressed the problem of wideband direction of arrival (DOA) estimation using grid-less sparse techniques, and these methods have been shown to outperform the traditional wideband DOA estimation methods. However, these methods often suffer from the problem of requiring manual parameter tuning or high computational complexity, which reduces their practicality. To alleviate this problem, a grid-less wideband DOA estimation method based on variational Bayesian inference is proposed in this paper. The method approximates the posterior probability density function of DOA with the help of variational Bayesian inference, which does not require manual adjustment of parameters and can obtain accurate DOA estimation results with low computational complexity. Numerical simulations and real measurement data processing show that the proposed method has a higher DOA estimation accuracy than other grid-less wideband methods while providing higher computational speed.
最近许多研究致力于利用无网格稀疏技术解决宽带到达方向（DOA）估计问题，并且这些方法已被证明优于传统的宽带DOA估计方法。然而，这些方法常常存在需要手动参数调整或计算复杂度高的问题，这降低了它们的实用性。为缓解这一问题，本文提出了一种基于变分贝叶斯推理的无网格宽带DOA估计方法。该方法借助变分贝叶斯推理来逼近DOA的后验概率密度函数，无需手动调整参数，并且能够以低计算复杂度获得准确的DOA估计结果。数值模拟和实际测量数据处理表明，所提方法在提供更高计算速度的同时，比其他无网格宽带方法具有更高的DOA估计精度。
