To enhance the operational security of offshore wind farms, a novel passive acoustic monitoring (PAM) technique has been developed to detect vessels that are not broadcasting automatic identification system (AIS) signals. Propeller activity generates unique low-frequency sound patterns that are mixed with ambient ocean noise. An improved Hilbert-Huang Transform is proposed to analyze underwater recordings and enhance propeller-related acoustic features. Based on the extracted blade-rate frequency components from the propeller noise, over 80% of ships could be detected when sailing near the hydrophone. The field experiment was conducted near the offshore wind farm in Taiwan, where a self-recording hydrophone was mounted on the seabed for 16 days. The sampling frequency was set to 192 kHz, and the hydrophone recorded continuously. The collected acoustic data were processed by the proposed method and compared with AIS records within 1 km of the station confirmed the successful identification of the vessels operating in the area. This PAM-based system offers a crucial, independent layer of surveillance, significantly improving maritime domain awareness and the overall safety of offshore wind installations. The hydrophone and proposed method can also be deployed on an unmanned surface vehicle to extend the surveillance area.
Chen et al. (Wed,) studied this question.