National Natural Science Foundation of China (Grant No. 51309215)
Navigation is a critical requirement for the operation of Autonomous Underwater Vehicles (AUVs). To estimate the vehicle position, we present an algorithm using an extended Kalman filter (EKF) to integrate dead-reckoning position with acoustic ranges from multiple beacons pre-deployed in the operating environment. Owing to high latency, variable sound speed multipath transmissions and unreliability in acoustic measurements, outlier recognition techniques are proposed as well. The navigation algorithm has been tested by the recorded data of deep sea AUV during field operations in a variety of environments. Our results show the improved performance over prior techniques based on position computation.