Cooperative localization of marine vehicles using nonlinear state estimation

This paper investigates the problem of cooperative navigation of autonomous marine vehicles using range-only acoustic measurements. We consider the use of a single maneuvering autonomous surface vehicle (ASV) to aid the navigation of one or more submerged autonomous underwater vehicles (AUVs), using acoustic range measurements combined with position measurements for the ASV when data packets are transmitted. The AUV combines the data from the surface vehicle with its proprioceptive sensor measurements to compute its trajectory. In previous work, we presented an experimental demonstration of this approach, using an extended Kalman filter (EKF) for state estimation. In the present paper, we analyze the observability properties of the cooperative ASV/AUV localization problem and present experimental results comparing several different state estimators. Using the weak observability theorem for nonlinear systems, we demonstrate that this cooperative localization problem is best attacked using nonlinear least squares (NLS) optimization. We present experimental results for this new approach and compare it to alternative state estimators, demonstrating superior performance