A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques

Abstract

In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.

A Pretreatment Method for the Velocity of DVL Based on the Motion Constraint for the Integrated SINS/DVL

“…Several self-adaptive filters have been presented from the aspects of error parameter modeling. Optimal estimation can be obtained only when the model and statistical parameters are consistent with actual ones [3][4][5][6]12,14]. However, how to restrain random noise with mathematical methods has received little attention.…”

“…In Equation (14), only the error in SINS is considered, so the system equations of SINS/MCP and SINS/PS are the same with that of SINS/DVL. The measurement vector of SINS/MCP and H SINS/MCP ptq can be constructed as:…”

An Adaptive Initial Alignment Algorithm Based on Variance Component Estimation for a Strapdown Inertial Navigation System for AUV

“…Therefore, the unscented Kalman filter (UKF) was proposed. With the unscented transformation (UT), UKF can approximate the mean and the variance of the Gaussian state distribution using the nonlinear function, avoiding the local linearization and the calculation of the Jacobian matrix effectively [2,18,19]. However, the covariance matrix is non-positive in high-dimensional systems sometimes, which will lead to filtering divergence.…”

“…However, pure inertial navigation cannot satisfy the demands of long range and long endurance, due to the navigation error, which accumulates with time. Thus, a typical navigation scheme for AUVs is based on a high quality SINS combined with Doppler velocity log (DVL) [2][3][4].…”

Model-Aided Navigation with Sea Current Estimation for an Autonomous Underwater Vehicle

“…The main disadvantage in the use of an INS (particularly with low-cost sensors) is the error growth in the position estimates due to the dead-reckoning nature of the method [2]. Traditionally, external sensors, such as GPS [3,2], transponders from a time of flight acoustic navigation method [4], Doppler velocity logs (DVLs) [5,6], sonar [7,8] or a camera [9,10], have been used to aid the solution of the inertial navigator, thus constraining the errors in position estimates. These external sensors have several practical disadvantages, essentially relating to the reliance on external information, such as the reception of satellite transmissions or a reliable and observable ocean floor.…”