Cooperative Distributed Estimation and Control of Multiple Autonomous Vehicles for Range-Based Underwater Target Localization and Pursuit

We study the problem of using single or multiple cooperative autonomous vehicles, called trackers, to localize and pursue an unknown underwater moving target using measurement of the ranges between the tracker(s) and the target. At the motion planning level, each tracker is assigned a spatial-temporal (S-T) curve to track that is generated by the composition of two types of motion: along the target's trajectory and on a path encircling the target. At the control level, we derive control laws for robust trajectory tracking and show that under mild assumptions on the convergence of the target's state estimate provided by a suitably designed filter, the tracker converges to and remains in a desired vicinity of the target. For the case of multiple trackers, we propose an efficient distributed estimation and control (DEC) strategy for the trackers that consider explicitly the constraints on the intertracker communication network. To this end, a distributed extended Kalman filter (DEKF) and a distributed control law for cooperative S-T curve tracking are developed to cooperatively pursue and localize the target. Using this setup, all trackers converge to a specified vicinity of the target while keeping an optimal tracker-target relative geometry that maximizes the range information acquired to estimate the target's state. The stability of the complete closed-loop DEC system is analyzed rigorously and the efficacy of the proposed strategy is illustrated with extensive computer simulations for the 2-D and 3-D cases.