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Talk: 07-Huang

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Talk-07: Autonomous PAM tracking system based on MOOS-IvP

Yen-Hsiang Huang1, Chi-Fang Chen1,2, Chin-Tang Hung1, You-Cheng Zhang1, Hsu-Yong Hung1, I-Yun Su1

(1) Department of Engineering Science and Ocean Engineering, National Taiwan University (2) Ocean Technology Research Center, College of Engineering, National Taiwan University

As the green energy concept grows, Taiwan is actively promoting the project “Thousand Wind Turbines” since 2012. However, the expected development area for potential offshore wind farms has overlapped with the habitat of the Sousa chinensis (also called Indo-Pacific Humpback Dolphin or Chinese White Dolphin). The impact noise from the pile driving process will influence marine mammals at specific ranges from pile driving spot. Therefore, the most important thing is to know if there are dolphins in the construction area or not.

This talk provides a process for checking if there are dolphins in the specific area and using an unmanned surface vehicle to track the source and find the area they are. The process system is built on MOOS-IvP. It has 4 main topics, including real-time dolphin whistle detection, real-time shoreside monitoring system, underwater acoustic source localization and source tracking behavior of the surface vehicle. First, the dolphin whistle detection system can be used in real-time not only for post processing and we use an image processing method to find the whistle pattern from the time frequency analysis and shows worth reference result of detection. Second, the acoustic voltage data which contain candidate whistle will be sent back by the system and plot the spectrogram autonomously in the shore side system for the artificial double check. The shoreside monitoring system also provide a GUI interface for more clear information. The left hand side of Figure 1 shows the underwater spectrogram and the detection result real-time and the right hand side shows pMarineViewer MOOS app. Third, the vehicle’s payload computer will start to calculate the bearing of source by two hydrophones and the sound pressure level after band-pass filter as the control parameter of the heading and the speed of the vehicle. Fourth, according to the bearing angle, the system will make the tracking strategy.

The whole process was implemented and tested in the river for several times. The performance of the detection system works well and has high detection rate and low false alarm rate. Over all, the result shows high consistent between simulation and field test, and the source tracking behavior is strongly influenced by the accuracy of the bearing. This work is supported by Department of Engineering and Technologies, Ministry of Science and Technology.

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