Song H C, Byun Gihoon
Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA.
J Acoust Soc Am. 2022 Apr;151(4):2336. doi: 10.1121/10.0009828.
Traditional matched-field processing (MFP) refers to array processing algorithms, which fully exploit the physics of wave propagation to localize underwater acoustic sources. As a generalization of plane wave beamforming, the "steering vectors," or replicas, are solutions of the wave equation descriptive of the ocean environment. Thus, model-based MFP is inherently sensitive to environmental mismatch, motivating the development of robust methods. One such method is the array invariant (AI), which instead exploits the dispersion characteristics of broadband signals in acoustic waveguides, summarized by a single parameter known as the waveguide invariant β. AI employs conventional plane wave beamforming and utilizes coherent multipath arrivals (eigenrays) separated into beam angle and travel time for source-range estimation. Although originating from the ideal waveguide, it is applicable to many realistic shallow-water environments wherein the dispersion characteristics are similar to those in ideal waveguides. First introduced in 2006 and denoted by χ, the dispersion-based AI has been fully integrated with β. The remarkable performance and robustness of AI were demonstrated using various experimental data collected in shallow water, including sources of opportunity. Further, it was extended successfully to a range-dependent coastal environment with a sloping bottom, using an iterative approach and a small-aperture array. This paper provides an overview of AI, covering its basic physics and connection with β, comparison between MFP and AI, self-calibration of the array tilt, and recent developments such as adaptive AI, which can handle the dependence of β on the propagation angle, including steep-angle arrivals.
传统的匹配场处理（MFP）是指一类阵列处理算法，这类算法充分利用波传播的物理特性来定位水下声源。作为平面波波束形成的一种推广，“导向矢量”或复制信号是描述海洋环境的波动方程的解。因此，基于模型的匹配场处理本质上对环境失配敏感，这促使了稳健方法的发展。其中一种方法是阵列不变量（AI），它利用声波导中宽带信号的色散特性，用一个称为波导不变量β的单一参数来概括。阵列不变量方法采用传统的平面波波束形成，并利用分离为波束角度和传播时间的相干多径到达（本征射线）来估计源距。尽管它起源于理想波导，但它适用于许多实际的浅水环境，在这些环境中色散特性与理想波导中的相似。基于色散的阵列不变量方法于2006年首次引入，用χ表示，现已与β完全整合。利用在浅水中收集的各种实验数据，包括机会源，证明了阵列不变量方法具有卓越的性能和稳健性。此外，通过迭代方法和小孔径阵列，它成功地扩展到了底部倾斜的距离相关海岸环境。本文概述了阵列不变量方法，涵盖其基本物理原理及其与β的联系、匹配场处理与阵列不变量方法的比较、阵列倾斜的自校准以及诸如自适应阵列不变量方法等最新进展，自适应阵列不变量方法可以处理β对传播角度（包括陡角到达）的依赖性。
