Research On Key Technologies Of Small AUV's Underwater Navigation System

The dissertation has investigated several key technologies of underwater integrated navigation system of small AUV (Autonomous Underwater Vehicles). They were devices error parameters identification technique and their noise filtering technique, attitude determination of AHRS (Attitude and Heading Reference System), and the fusion strategy of integrated navigation system.Owing to restrictions on hull size and cost of small AUV, its integrated navigation system always adopts MEMS inertial devices, which have small volume, low cost and low power consumption, besides small, low power consumption Doppler velocity log. These navigation devices can meet the requirements of mechanical and electrical characteristics of small AUV, although they often have low accuracy. The integrated navigation system consists of these devices has poor performance, and even affects the small AUV's guidance and control system stability. The first half of dissertation focused on the above problems of the navigation equipment used by small AUV.First of all, after calibrating deterministic error of MEMS inertial devices, Allan variance recursive identification algorithm was derived using overlapping and direct sampling technique respectively, founded on classic Allan variance identification technique, which made it possible to identify the parameters of random noise online for each MEMS navigation devices.Then the noise model of phased-array Doppler velocity log adopted by small AUV's navigation system was built by time series analysis technique. Referencing the S plane control algorithm, the Kalman filter to Doppler velocity log was designed, suitable for the small AUV.At last, strong tracking UKF algorithm based on unscented transformation was designed, using in the small AUV's compass calibration process. The strong tracking algorithm was introduced into UKF completely based on UT technique, and the algorithm of its fading factor matrix was improved, meanwhile estimator of noise parameters was also introduced into this algorithm, so that made the algorithm have good adaptability and robustness. The algorithm solved the wave interference problem, and improved the recognition ability of error parameters of UKF algorithm, when small AUV's compass calibrating on the sea surface.In the following chapters, angular rate input attitude determination solution was designed based on FFT (Fast Fourier Transformation) algorithm, using continuous signal decomposition and reconstruction technique in frequency domain. The algorithm was programmed in Matlab and compared with the quaternion attitude determination solved by fourth order Runge-Kutta algorithm in time domain. The comparison showed that frequency domain algorithm can reduce the coning error effectively, and improve the accuracy of attitude determination.Although the accuracy of electronic compass in integrated navigation system is higher, it is easily interfered by non-gravitational acceleration, causing jump error in output attitude signal. Meanwhile, the heading and attitude information solved by MEMS gyro unit is not easily spoiled by non-gravitational acceleration, but its accumulation error is large. Based on the above characteristics, the attitude information respectively solved by electronic compass and MEMS gyro unit was fused by adaptive weighted algorithm, so that the attitude and heading information by electronic compass was smoothed, and the dynamic performance of the whole AHRS was improved as well.In the last part of this thesis, an adaptive UKF algorithm with model error was designed to reduce the navigation system model error caused by ocean current disturbance. Based on the idea of virtual noise, this algorithm used suboptimal MAP estimator to calculate statistics of virtual noise real time, which helped to improve the accuracy of navigation system and to enhance the ability of filtering.