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.
Highlights
With the development of high-frequency, multi-beam Doppler sonar, which can provide bottom velocity measurements with a precision of 0.3% or less with a update rate of up to 5Hz, a wide variety of Doppler-based navigation techniques have been developed [1]
If the filtering epoch is smaller than the sampling window size of innovation or residual, Adaptive UKF (AUKF) is working at the mode of Unscented Kalman Filter (UKF)
It means that the distinctions between the performances of the UKF and the AUKF are small if a appropriate measurement noise covariance
Summary
With the development of high-frequency, multi-beam Doppler sonar, which can provide bottom velocity measurements with a precision of 0.3% or less with a update rate of up to 5Hz, a wide variety of Doppler-based navigation techniques have been developed [1]. There are mainly two alignment schemes to solve this problem for small misalignments [5,6].The first method is to establish the INS error dynamics in the body frame, so the velocity of the Doppler can be used as the measurements directly [5]. It can be used as the measurements for the Kalman filter. The other main effort to deal with the large misalignments problem is based on such nonlinear filtering methods as the so-called extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). This study first evaluates the impacts of adaptive filtering methods on the parameter estimation stability with different window sizes and different initial measurement noise covariance matrix.
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