This paper presents a hybrid navigation system for underwater robotic vehicles to track precisely in rough sea environment conditions. The tracking system is composed of multi-sensor systems such as an inclinometer, a tri-axis magnetometer, a flowmeter, and an super short base line(SSBL) acoustic navigation system. Due to the inaccuracy of the attitude sensors, the heading sensor with measurement errors, and the flowmeter, the predicted position slowly drifts and the estimation error of position becomes larger. On the other hand, the measured position is liable to change abruptly due to the corrupted data of the SSBL system in the case of low signal to noise ratio or large ship motions. By introducing a sensor fusion technique with the position data of the SSBL system and those of the attitude heading flowmeter reference system (AHFRS), the hybrid navigation system updates the three-dimensional position robustly. A Kalman filter algorithm is derived on the basis of the error models for the flowmeter dynamics with the use of the external measurement of the SSBL. A failure detection algorithm decides the confidence degree of external measurement signals by using a fuzzy inference. Simulation is included to demonstrate the validity of the hybrid navigation system.
The current approach to cost-effective achievement of highaccuracy navigation in underwater vehicle is a system that include one or more inertial navigation systems(INS), and one or more navigation reference sensors such as a global positioning system (GPS) receiver, flowmeter, depthmeter, heading sensor, and positioning sonar system. Functional integration of INS and GPS can improve the performance of vehicles and have actively processed during last decade in aerospace engineering fields (Bruinback and Srinath. 1987. Chen and Chui, 1990 and Da, 1994).