Abstract:
This paper explores the use of 'hybrid' state estimators to increase the accuracy and flexibility
of acoustic position measurement systems used in the control of underwater vehicles.
Two different approaches to extend the range of acoustic position measurement systems are
explored. The first approach is the use of an inexpensive Strapdown Inertial Measurement
System (SIMS) to augment the acoustic with inertial position information. This approach is
based on the experience gained using an attitude and inertial measurement package fielded
on the JASON JUNIOR Remotely Operated Vehicle (ROV). The second approach is the use
of a mobile, platform-mounted, acoustic net in conjunction with a platform tracking system.
This second investigation used the JASON ROV as the basis for the simulation work. As motivation,
some of the theoretical and practical difficulties encountered when range is extended
using an unaugmented system are explored. Simulation results are used to demonstrate the
effects of these difficulties on position estimation accuracy and on the performance in closed
loop control of the vehicle. Using measured sensor noise characteristics, a hybrid Kalman
filter is developed for each approach to take the greatest advantage of the available information.
Formulation of the Kalman filter is different for each case. In the second case, the
geographic position of the ROV is the sum of the acoustic net's geographic position, measured
at a different interval by an RF positioning system, and the position of the ROV relative to
the net, as measured acoustically. Closed loop vehicle performance evaluations are made for
representative noise levels and update rates with and without the augmentation discussed in
the first approach. Finally, conclusions are drawn about the benefits and applications of the
hybrid Kalman filter to the control of Remotely Operated Vehicles.
