Data Fusion Using OPELM for Low-Cost Sensors in AUV

With mobility, security, intelligence and other advantages, autonomous underwater vehicle (AUV) becomes an indispensable instrument in the complex underwater environment. Owing to the independence of external signal (such as GPS) which is restricted or invalid in the water, inertial navigation system (INS) has become the most suitable navigation and positioning system for Underwater Vehicles. However, as the excessive reliance of sensor data, the precision of INS can be affected by sensor data especially heading angle data from low-cost sensor such as attitude and heading reference system (AHRS) and digital compass. Therefore, how to fuse low-cost sensor information to get more accurate data becomes the key to improve navigation accuracy. Based on the original Extreme Learning Machine (ELM) algorithm, the Optimally Pruned Extreme Learning Machine (OPELM) algorithm is presented as a more robust and general methodology in 2010, which make it possible to realize data fusion by using a more reliable network. In this paper, we proposed a method of data fusion which using Optimally-Pruned Extreme Learning Machine (OPELM) to improve the accuracy of heading angle from AHRS and digital compass. Our method has already been demonstrated by a range of real datasets, and it outperforms current available Kalman Filtering algorithms in efficiency.