Zhao Anbang, Wang Keren, Hui Juan, Song Pengfei, Guo Jiabin
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China.
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China.
J Acoust Soc Am. 2024 Mar 1;155(3):2000-2013. doi: 10.1121/10.0025287.
Estimating the direction of arrival (DOA) of spatially spread sources is a significant challenge in array signal processing. This work introduces an effective method within the sparse Bayesian framework to tackle this issue. A spatially spread source is modeled using a multi-dimensional Slepian signal subspace that expands the dictionary and results in a block-sparse structured solution. By taking advantage of block-sparse Bayesian learning, parameter estimation becomes feasible. A complex Gaussian posterior is derived under a multi-snapshot block-sparse framework with a complex Gaussian prior and varying noise conditions. The hyperparameters are estimated using the expectation-maximization algorithm. Through numerical tests and sea test data evaluations, the proposed method shows superior energy focusing for spatially spread signals. Under limited snapshots and challenging signal-to-noise ratios, the current method can still offer precise DOA determination for spatially spread sources.
估计空间分布源的到达方向（DOA）是阵列信号处理中的一项重大挑战。这项工作在稀疏贝叶斯框架内引入了一种有效方法来解决这一问题。使用多维斯莱皮安信号子空间对空间分布源进行建模，该子空间扩展了字典并产生块稀疏结构的解。通过利用块稀疏贝叶斯学习，参数估计变得可行。在具有复高斯先验和变化噪声条件的多快拍块稀疏框架下推导了复高斯后验。使用期望最大化算法估计超参数。通过数值测试和海试数据评估，所提出的方法对空间分布信号显示出卓越的能量聚焦。在有限快拍和具有挑战性的信噪比条件下，当前方法仍可为空间分布源提供精确的DOA确定。
