Shallow-sea application of an intelligent fusion module for low-cost sensors in AUV

Abstract

This paper focuses on the application of AUV in shallow-sea, which environment is more complicated than deep-sea. Owing to independence of external signals, inertial navigation system (INS) has become the most suitable navigation and positioning system for underwater vehicles. However, as the excessive reliance on sensor data, the precision of INS can be affected by external environment, especially heading angles from low-cost sensors such as attitude and heading reference system (AHRS) and digital compass are susceptible to waves and magnetic interference. Therefore, how to use data from low-cost sensors becomes the key to improving navigation performance. Optimally pruned extreme learning machine (OP-ELM) was presented as a more robust and general methodology in 2010, which make it possible to fuse data by using a more reliable method. In this paper, we propose an intelligent fusion module which is designed to obtain the full-noise model for AUV. By judging the state of AHRS and TCM heading angles, intelligent fusion module combines full-noise model with credible data by using OP-ELM to improve the accuracy of positioning and navigation. Our method has been demonstrated by a range of real data, which RMSE can at most improve by 86.4% in complex conditions than Extended Kalman Filter's.