Implementation of Underwater Vessel Noise Localization Using Vector Array Near-Field Focusing

This paper discusses methods for implementing underwater vessel noise localization and how to improve localization accuracy and precision through vector array near-field focusing. During implementation, the complexity of the underwater environment and noise sources must be considered. Therefore, various technical approaches can be employed, such as signal processing algorithms (including filtering and spectral analysis), sound source localization techniques (like Time Difference of Arrival - TDOA), and data analysis methods to optimize the localization algorithm. The implementation typically involves array signal processing where the vector sensors capture both acoustic pressure and particle velocity information, enabling more accurate direction-of-arrival estimation. Furthermore, the research requires in-depth exploration of the principles and applications of the vector array near-field focusing algorithm to better understand its role in underwater noise localization. The algorithm implementation usually involves calculating the steering vectors for near-field scenarios and applying appropriate beamforming techniques. In future research, we can further explore deep learning-based underwater noise localization algorithms using neural networks for pattern recognition and regression analysis, which could potentially improve localization accuracy and robustness by learning complex underwater acoustic propagation patterns.