Date of Award
MS in Computer Science
Presented is a method for estimating the planar position, velocity, and orientation states of a tagged shark. The method is designed for implementation on an Autonomous Underwater Vehicle (AUV) equipped with a stereo-hydrophone and receiver system that detects acoustic signals transmitted by a tag. The particular hydrophone system used here provides a measurement of relative bearing angle to the tag, but does not provide the sign (+ or -) of the bearing angle. A particle filter was used for fusing measurements over time to produce a state estimate of the tag location. The particle filter combined with an active control system allowed the system to overcome the ambiguity in the sign of the bearing angle. This state estimator was validated by tracking a stationary tag and moving tag with known positions. These experiments revealed state estimate errors were on par with those obtained by manually driven boat based tracking systems, the current method used for tracking fish and sharks over long distances. Final experiments involved the catching, releasing, and an autonomous AUV tracking of a 1 meter leopard shark (Triakis semifasciata) in SeaPlane Lagoon, Los Angeles, California.