System noise variance matrix adaptive Kalman filter method for AUV INS/DVL navigation system

Abstract

The Inertial Navigation System (INS)/Doppler Velocity Log (DVL) navigation system is capable of locating the Autonomous Underwater Vehicle (AUV) in real-time. However, inherent errors of sensors, especially the inertial measurement unit (IMU) measurement noise changes during navigation, make the system noise variance matrix unknown and time-dependent, resulting in inaccurate Kalman filter estimates of velocity and reducing the accuracy of the navigation system. To solve this problem, this paper proposes an adaptive system noise variance matrix Kalman Filter method for AUV INS/DVL navigation system. In this method, the statistical characteristics of the IMU measurement noise are estimated by distinguishing the AUV motion information obtained by the IMU measurement and the IMU measurement noise by the frequency domain analysis method. The Kalman filter velocity estimation accuracy is improved by adaptively adjusting the system noise variance matrix based on the IMU measurement noise. Six different sets of trajectory experiments were used to validate the effectiveness and applicability of the algorithm proposed in this paper. The experimental results show that the improved KF algorithm improves the positioning accuracy by an average of 2.29‰ navigation distance compared with the traditional KF algorithm, which proves that the proposed method can improve navigation performance.