Research On Data Fusion Method In The Recovery Process Of AUV For Motion Ship

Autonomous Underwater Vehicles (AUVs) play an important role in undertaking many missions, such as the exploration of the oceans and military applications. There is an energy supplement limitation for underwater vehicle movement. After completing a certain task, it has to go back and supply the energy. Among many existing recovery methods, underwater recovery process of AUV can significantly improve AUV underwater operation time and effectiveness with low risk. The main content of this research is the underwater recovery technology which utilizes multi-sensor data fusion methods.Not only absolute position of AUV itself, but also the relative position between motion ship and AUV need to be known during underwater recovery process of AUV. Multi-sensor fusion technology can be used to solve the above problems. In this dissertation we design data fusion location system for finding the target stage automatically and accurately docking of underwater recovery. Based on data fusion location system of automatically finding the target stage, the relative position between motion ship and AUV can be gotten. Moreover, during data fusion location system of accurately docking stage, we can know the precise position of AUV itself.There are some error data in the sensor caused by noise before the multi-sensor data fusion process, preprocessing needs to be done to the error data. In this dissertation, we propose adaptive filtering method, which is better than traditional technique, meeting real-time requirement. An experiment is carried out in a water tank and shows good filtering effect. Then, filtering data is made time and special rectification in order to facilitate data fusion process.This dissertation has been focused on the data fusion in the underwater recovery process of AUV using motion ship; furthermore, much attention is given to the weighted mean, Extended Kalman Filter (EKF) and Unscented Kalman Filter UKF principals and algorithms. In the meanwhile, we describe the state equations and measurement equations with respect to finding the target stage automatically in the process of underwater recovery.In this dissertation, we design data fusion method based on EKF and UKF to find the target stage automatically of underwater recovery, and propose data fusion using weighted mean for accurately docking of underwater recovery. We demonstrate the practicality of this approach using data sets gathered in experiments and carry out experiment in water tank and lake. The results verify the validity of our approach. It proves that the navigation system based-on UKF is more precise, SBL and vision based-on weighted mean is better than any single sensor data. The data fusion technique can be applied in the underwater recovery of AUVs for motion ship with better effect.