An Adaptive Extended Kalman Filter for State and Parameter Estimation in AUV Localization

Given their independence from operators and potentially unrestricted range of operations, Autonomous Underwater Vehicles (AUVs) are considered key enablers of a host of applications of the Blue Economy. A critical requirement for AUVs is that of being able to self-localize so that the data they collect are clearly marked with position information. Localization is challenging underwater, as GPS and other technologies that use radio frequencies do not work in water. This has brought to the development of solutions that often involve costly technology and operations that are impractical to use in many situations, such as when swift and affordable localization is required. In this paper, we present a method for localizing AUVs that lends itself to be used in such situations, while providing localization that is as accurate as that from more expensive methods. Our method is based on pre-deployed acoustic beacons (whose coordinates do not need to be known by the AUV) and on mainstream sensors usually available onboard most AUVs. It employs an adaptive Extended Kalman Filter (EKF) that exploits statistical techniques to overcome the inaccuracies of baseline EKF when the noise of the environment or of the instrumentation is time-varying or unknown. We demonstrate the effectiveness of our method for accurate AUV localization through simulations and experiments at sea with an AUV and commercial acoustic transducers. Our results show swift determination of the beacon positions and meter-level localization, suggesting that our method can be effectively used in most underwater applications.