Byun Gihoon, Song H C, Kim J S
Scripps Institution of Oceanography, La Jolla, California 92093-0238, USA.
Korea Maritime and Ocean University, Busan, 49112, South Korea.
J Acoust Soc Am. 2018 Dec;144(6):3067. doi: 10.1121/1.5080603.
This paper compares the localization performance of array invariant (AI) and matched field processing (MFP) using a ship of opportunity radiating random noise (200-900 Hz) and a tilted vertical array. AI is a deterministic approach to source-range estimation (i.e., depth-blind), exploiting the dispersion characteristics of broadband signals with minimal/no knowledge of the environment in shallow water. It involves time-domain plane-wave beamforming to separate multiple coherent arrivals (eigenrays) in beam angle and travel time, called "beam-time migration," from which the source range is directly estimated. In contrast, MFP is a model-based approach that requires accurate knowledge of the environment and array geometry (e.g., array tilt) to generate "replicas" for all possible source locations, finding the best match in the two-dimensional ambiguity surface of range and depth. While AI and MFP are both sensitive to array tilt, AI is equipped with self-calibration capability to estimate the array tilt and source range simultaneously. With the array tilt information from AI incorporated, the performance of MFP for range estimation can be comparable to that of AI to such an extent that the environmental knowledge is accurate.
本文使用一艘发射随机噪声（200 - 900赫兹）的机会船和一个倾斜垂直阵列，比较了阵列不变量（AI）和匹配场处理（MFP）的定位性能。AI是一种用于源距离估计的确定性方法（即深度盲估），它利用宽带信号的色散特性，在对浅水环境了解极少或完全不了解的情况下进行源距离估计。它涉及时域平面波波束形成，用于在波束角度和传播时间上分离多个相干到达信号（本征射线），这一过程称为“波束 - 时间偏移”，并直接从该过程中估计源距离。相比之下，MFP是一种基于模型的方法，需要精确了解环境和阵列几何形状（例如阵列倾斜度），以便为所有可能的源位置生成“副本”，在距离和深度的二维模糊表面中找到最佳匹配。虽然AI和MFP对阵列倾斜都很敏感，但AI具备自校准能力，能够同时估计阵列倾斜度和源距离。结合来自AI的阵列倾斜信息后，在环境知识准确的情况下，MFP在距离估计方面的性能可以与AI相媲美。
