Forero Pedro A, Baxley Paul A
Maritime Systems Division, Space and Naval Warfare Systems Center Pacific, 53560 Hull Street, San Diego, California 92152.
J Acoust Soc Am. 2014 Jun;135(6):3483-501. doi: 10.1121/1.4874605.
Using passive sonar for underwater acoustic source localization in a shallow-water environment is challenging due to the complexities of underwater acoustic propagation. Matched-field processing (MFP) exploits both measured and model-predicted acoustic pressures to localize acoustic sources. However, the ambiguity surface obtained through MFP contains artifacts that limit its ability to reveal the location of the acoustic sources. This work introduces a robust scheme for shallow-water source localization that exploits the inherent sparse structure of the localization problem and the use of a model characterizing the acoustic propagation environment. To this end, the underwater acoustic source-localization problem is cast as a sparsity-inducing stochastic optimization problem that is robust to model mismatch. The resulting source-location map (SLM) yields reduced ambiguities and improved resolution, even at low signal-to-noise ratios, when compared to those obtained via classical MFP approaches. An iterative solver based on block-coordinate descent is developed whose computational complexity per iteration is linear with respect to the number of locations considered for the SLM. Numerical tests illustrate the performance of the algorithm.
由于水下声学传播的复杂性，在浅水环境中使用被动声纳进行水下声源定位具有挑战性。匹配场处理（MFP）利用测量的和模型预测的声压来定位声源。然而，通过MFP获得的模糊度表面包含伪像，这限制了其揭示声源位置的能力。这项工作引入了一种用于浅水源定位的稳健方案，该方案利用了定位问题固有的稀疏结构以及表征声学传播环境的模型。为此，将水下声源定位问题转化为对模型失配具有鲁棒性的稀疏诱导随机优化问题。与通过经典MFP方法获得的结果相比，所得的声源位置图（SLM）即使在低信噪比下也能减少模糊度并提高分辨率。开发了一种基于块坐标下降的迭代求解器，其每次迭代的计算复杂度与SLM考虑的位置数量成线性关系。数值测试说明了该算法的性能。
