Study On Filter Arithmetic Of Long-range Autonomous Underwater Vehicle Integrated Navigation

Long range and the complex ocean conditions make navigation and position very important for the long-range Autonomous Underwater Vehicle (AUV). To improve the navigation precision, in addition to the high-powered hadewares, high-effectived and high-precised filtering arithmetic are effective which can restrain the speed of parameter errors. Based on "Underwater Navigition-water surface correction-Underwater Navigition" navigation mode, in which SINS/DVL integrated navigation is used as the underwaer navigation means and SINS/GPS integrated navigation is used as the water surface correction system. The integrated navigation system is designed by the improved Kalman filtering arithmetic, inverse-matrix-free kalman filtering arithmetic and improved adaptive kalman filtering arithmetic.And the performance is simulated through Matlab tools.Computer simulation results show that improved filter arithmetics have better precision , higher efficiency and higher numerical robustness. The main work of the thesis are as follows:1. Based on basic kalman filtering arithmetic, improved kalman filtering arithmetic is given. Filter gain K and forecase P are improved for enhance the ability of tracking AUV actions and robustness. The computer efficiency is increased by changing the inverse-matrix to the inverse-matrix-free. And the improved adaptive Kalman filtering is given on the basis of Suge-Husa adaptive Kalman filtering and strong tracking filtering.2. Using Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL) as the underwater navigation system. Based on the errors analysis of SINS/ DVL,the Kalman filter is designed with improved filter algorithmic and the filter has mix-structure that includes output correct and feedback correct.Simulation results show the filter has better precision, higher efficiency and higher numerical robustness.3. Using SINS and Global Positioning System(GPS) as the water-surface correction system. Based on the errors analysis of GPS, the kalman filter is designed with improved adaptive Kalman filtering and the filter has mix-structure including output correct and feedback correct . Simulation results show the filter has better precision and better anti-jamming abbility.4. The computer simulation is completed through Matlab tools. The simulation includes SINS/DVL integrated navigation system filter programs and...