JPDAFilter Design for Tracking Multiple Underwater Vehicles in 3D Space

Abstract

With continuous advances in ocean robotics and sensing technologies, the use of multiple underwater vehicles has attracted increasing attention in various scientific and engineering applications. Since GPS signals are not available under the water surface, navigation for multiple underwater vehicles typically relies on relative measurements between each vehicle and a GPS-equipped surface vehicle acting as a reference beacon. This study presents the design of a tracking filter for estimating the trajectories of multiple underwater vehicles in 3D space using such relative information. In particular, a joint probabilistic data association filter (JPDAF) is employed to achieve robust trajectory estimation in the presence of cluttered measurements caused by sensor noise, reflections from physical objects, or environmental disturbances. The performance of the JPDAF is evaluated and compared with that of a tracking filter based on the extended Kalman filter with using a nearest-neighbor data association approach through numerical simulations.