An Observability Metric for Underwater Vehicle Localization Using Range Measurements

The paper addresses observability issues related to the general problem of single and multiple Autonomous Underwater Vehicle (AUV) localization using only range measurements. While an AUV is submerged, localization devices, such as Global Navigation Satellite Systems, are ineffective, due to the attenuation of electromagnetic waves. AUV localization based on dead reckoning techniques and the use of affordable motion sensor units is also not practical, due to divergence caused by sensor bias and drift. For these reasons, localization systems often build on trilateration algorithms that rely on the measurements of the ranges between an AUV and a set of fixed transponders using acoustic devices. Still, such solutions are often expensive, require cumbersome calibration procedures and only allow for AUV localization in an area that is defined by the geometrical arrangement of the transponders. A viable alternative for AUV localization that has recently come to the fore exploits the use of complementary information on the distance from the AUV to a single transponder, together with information provided by on-board resident motion sensors, such as, for example, depth, velocity and acceleration measurements. This concept can be extended to address the problem of relative localization between two AUVs equipped with acoustic sensors for inter-vehicle range measurements. Motivated by these developments, in this paper, we show that both the problems of absolute localization of a single vehicle and the relative localization of multiple vehicles can be treated using the same mathematical framework, and tailoring concepts of observability derived for nonlinear systems, we analyze how the performance in localization depends on the types of motion imparted to the AUVs. For this effect, we propose a well-defined observability metric and validate its usefulness, both in simulation and by carrying out experimental tests with a real marine vehicle during which the performance of an Extended Kalman Filter state observer is shown to depend on the types of motion imparted to the vehicle.