Modeling of deep ocean ambient noise and sound-ray-arrival-grazing-angle estimation for shallow receivers

The ambient noise model of isotropic fields is not applicable to shallow-located platforms in the deep ocean, and the conventional methods for estimating the sound-ray-arrival-grazing-angle of targets do not account for the effects of surface noise. Therefore, target parameter estimation methods based on a single vector hydrophone are explored in this study. The approach used in this study integrates the hydroacoustic physical model, signal processing method, and ocean ambient. A time-domain model of the vector field of deep ocean ambient noise was developed for receivers positioned at shallow depths, followed by derivation of the covariance matrix of the single vector hydrophone based on this model. Subsequently, a target signal-to-noise ratio (SNR) estimation method using the covariance matrix derived from the single vector hydrophone was formulated. This method effectively addresses the challenge of distinguishing between target signal and noise by transforming the power estimation problem into a covariance matrix solving the task. Finally, a refined approach for estimating the sound-ray-arrival-grazing-angle of target is proposed, aiming to theoretically mitigate the impact of surface noise on the target signal. The experimental data obtained from a deep ocean region in the South China Sea indicate that the findings obtained using the method proposed in this study are consistent with the reference values derived from automatic identification system information. The method also demonstrates reliable estimation results even when the SNR exceeds -5 dB. The conceptual framework developed for SNR and sound-ray-arrival-grazing-angle estimation in this study can be readily applied to other ambient models, indicating potential applications in engineering field. The primary objective of the study was to enhance and augment underwater acoustic signal processing methods for shallow receivers deployed in the deep ocean.