Autonomous underwater acoustic localization through multiple unmanned surface vehicle

Since 2016, the Taiwan government has started the Thousand Wind Turbines Project, the goal of which is to build one thousand offshore wind turbines and provide 5.6GW power by the year 2025. On July 2nd, 2015, The Bureau of Energy, Ministry of Economic Affairs, R.O.C announced there would be 36 potential zones for wind farms in the Taiwan Strait near the seacoast. However, most of these potential zones are close to the habitat of the critically endangered species, Sousa Chinensis. To avoid the hearing damage (temporary threshold shift and permanent threshold shift) caused by the noise of construction, including pile driving noise and operating noise, the observation of cetaceans around the construction site throughout the period of piling is important. The traditional method of observation is that the marine mammal observer (MMO) works on the vehicle and searches for the cetaceans with his naked eyes. Yet this method becomes inefficient and restricted whenever the cetacean breaks the water surface. Therefore, this research provides an advanced method using passive acoustic monitoring (PAM) and multiple unmanned surface vehicle (USV) to enhance the efficiency of observation. In this research, the whole system was built in C++ and Python as well as two popular open-source middleware - ROS (Robot Operating System) and MOOS-IvP, both of which are software for robotics and have a similar architecture to develop applications. More precisely, MOOS-IvP is software for autonomous marine vehicles. In order to make good use of the coordination of ROS and MOOS-IvP, this research built a bridge of communication connecting the two. The robotic USV system consists of two computers, namely, the front seat and the back seat. The front seat, installed in a vehicle, works by receiving the data from sensors and controlling the motors. The back seat, an additional computer connected with the front seat, is in charge of mission management, making decisions based upon the data from the front seat and commands the front seat where to move. Each of the two unmanned surface vehicles, equipped with two hydrophones under the ship hull, are used to localize the underwater acoustic source in this research. The two-channel hydrophone data is used to calculate the bearing angle of USV from an underwater acoustic source through the time difference of arrival method (TDOA). TDOA utilizes the receiving time of the first arrival of the acoustic wave to get the time difference between two hydrophones. Through such time difference, plus sound speed, and the distance between two hydrophones, the bearing angle can be approximatively estimated. Besides, the source localization can also be estimated by the intersection of two bearing lines from each USV. The whole system and algorithm were tested at Shimen Reservoir, Taoyuan, Taiwan. The water depth of the reservoir was around 12 meters, and an underwater acoustic projector was placed at the water depth of 5 meters to project an artificial chirp signal similar to the whistling sounds emitted by Sousa Chinensis. Two USVs surveyed a certain area to acquire the acoustic data. After acquiring the data, USV sent the result of the bearing angle to the shoreside computer, which would then estimate the source position by combining the results from both USVs.