Aided Strapdown Inertial Navigation for Autonomous Underwater Vehicles

This paper presents a navigation algorithm based on aided strapdown inertial navigation (INS) for an underwater autonomous underwater vehicle (AUV). The AUV is equipped with a long baseline (LBL) acoustic positioning system, acoustic Doppler current profiler (ADCP) and a depth sensor to aid the INS. They have, however, much slower data rates than that of the INS. A linearized, quaternion-based dynamic model and measurement model of the INS output errors are presented. Data from different sensors are fused by applying the extended Kalman filer (EKF) to estimate and correct the errors. Due to the difficulty of generating realistic simulation scenario, real data (raw INS measurement) collected from AUV field experiments are processed to test the algorithm. Without knowing the ground truth, however, performance evaluation becomes much more complicated and needs further research. In this paper, the problem is circumvented by considering the post-processed real data as the "ground truth" and noisy raw measurements are generated from this "ground truth" to feed the algorithm. The simulation results demonstrate the algorithm applicability and show that by incorporating readings from the ADCP and the depth sensor, the (horizontal) position errors still increase but with a significant lower rate than the case of stand-alone operation. If the LBL sensor is further included, the navigation errors can be constrained within a certain bound.