Distributed Estimation of a Flow Field using Cooperative Underwater Vehicles

This paper presents a distributed method of cooperative flow field estimation using a group of underwater vehicles in GPS-denied environments without directly measuring the ambient flow velocity. We consider that the vehicle can measure the relative positions of its neighbors and the absolute position when it surfaces. By formulating the measurements into the relative and absolute motion-integration error constraints, the flow field estimation problem is converted into an inverse problem that solves a determined system of nonlinear equations in a distributed way. We then propose a distributed consensus algorithm to solve the above equations, in which each vehicle first shares local flow estimate with its neighbors, and then updates the estimate using local constraints. The convergence of the proposed algorithm is strictly guaranteed, and simulations are provided to validate its effectiveness.