A multi-AUV state estimator for determining the 3D position of tagged fish

This paper presents a multi-AUV state-estimator that can determine the 3D position of a tagged fish. In addition to angle measurements, the state-estimator also incorporates distance and depth measurements. These additional sensor measurements allow for greater accuracy in the position estimates. A newly developed motion model that better accounts for multiple hypotheses of the motion of a tagged fish is used to increase the robustness of the state-estimator. A series of multi-AUV shark tracks were conducted at Santa Catalina Island, California over the span of four days to demonstrate the ability of the state-estimator to determine the 3D position of a tagged leopard shark. Additional experiments in which the AUVs tracked a tagged boat of known location were conducted to quantify the performance of the presented state-estimator. Experimental results demonstrate a three-fold decrease in mean state-estimation error compared to previous works.