Navigation Scheme for the LSTS SEACON Vehicles: Theory and Application

Abstract

Underwater Systems and Technology Laboratory (LSTS) vehicles used an Extended Kalman Filter (EKF) navigation scheme that considered global positioning system (GPS), long-baseline (LBL) and the propeller's angular velocity sensor readings information. Although an attitude, heading and reference system (AHRS) and a doppler velocity logger (DVL) sensors were available in the latest generation of vehicles, that information was not used in the original EKF algorithm. A new filter was designed that considers GPS, LBL, inertial measurement unit (IMU), AHRS and DVL data. This Navigation scheme, although supporting LBL data information, aims at a fully autonomous dead reckoning operation, relying only on “proprioceptive” sensors, since the next generation of LSTS LAUV Seacon vehicles will carry a high precision, low drift error rate IMU. This paper presents the theory involved in this EKF, as well as other preconditioning functions applied to extract good data from noisy sensor readings. Simulation and real mission results are presented to validate the approach.