用于稳健匹配场处理的多频稀疏贝叶斯学习
Multi-frequency sparse Bayesian learning for robust matched field processing.
作者信息
Gemba Kay L, Nannuru Santosh, Gerstoft Peter, Hodgkiss William S
机构信息
Marine Physical Laboratory of the Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California 92093-0238, USA.
出版信息
J Acoust Soc Am. 2017 May;141(5):3411. doi: 10.1121/1.4983467.
The multi-snapshot, multi-frequency sparse Bayesian learning (SBL) processor is derived and its performance compared to the Bartlett, minimum variance distortionless response, and white noise constraint processors for the matched field processing application. The two-source model and data scenario of interest includes realistic mismatch implemented in the form of array tilt and data snapshots not exactly corresponding to the range-depth grid of the replica vectors. Results demonstrate that SBL behaves similar to an adaptive processor when localizing a weaker source in the presence of a stronger source, is robust to mismatch, and exhibits improved localization performance when compared to the other processors. Unlike the basis or matching pursuit methods, SBL automatically determines sparsity and its solution can be interpreted as an ambiguity surface. Because of its computational efficiency and performance, SBL is practical for applications requiring adaptive and robust processing.
推导了多快照、多频率稀疏贝叶斯学习（SBL）处理器，并将其性能与用于匹配场处理应用的巴特利特、最小方差无失真响应和白噪声约束处理器进行了比较。感兴趣的双源模型和数据场景包括以阵列倾斜形式实现的实际失配以及与副本向量的距离-深度网格不完全对应的数据快照。结果表明，在存在较强源的情况下定位较弱源时，SBL的行为类似于自适应处理器，对失配具有鲁棒性，并且与其他处理器相比，其定位性能有所提高。与基方法或匹配追踪方法不同，SBL会自动确定稀疏性，其解决方案可解释为一个模糊表面。由于其计算效率和性能，SBL对于需要自适应和鲁棒处理的应用来说是切实可行的。
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