Abstract
The Kalman Filter algorithm usually cannot estimate noise statistics in real-time, in order to deal with this issue, a new kind of improved Adaptive Extended Kalman Filter algorithm is proposed. Based on residual sequence, this algorithm mainly improves the adaptive estimator of the filter algorithm, which can estimate measurement noise in real-time. Furthermore, this new filter algorithm is applied to a SINS/GPS loosely-coupled integrated navigation system, which can automatically adjust the covariance matrix of measurement noise as noise varies in the system. Finally, the original Extended Kalman Filter and the improved Adaptive Extended Kalman Filter are applied respectively to simulate for the SINS/GPS loosely-coupled model. Tests demonstrate that, the improved Adaptive Extended Kalman Filter reduces both position error and velocity error compared with the original Extended Kalman Filter.
Highlights
Strap-down inertial navigation system (SINS) does not need external input
The simulation results of velocity error and position error are shown in Fig.3 and Fig
The velocity mean error as well as position mean error shown in Tab.1 and Tab.3 of ENU calculated by Adaptive Extended Kalman Filter (AEKF), which are smaller than those calculated by Extended Kalman Filter (EKF)
Summary
Strap-down inertial navigation system (SINS) does not need external input. By its own inertial sensors, it can get the navigation information of the carrier [1]. The Adaptive Kalman Filter can adjust the process noise covariance matrix and (or) measurement noise covariance matrix in real-time. It can solve the problem of estimation error increasing and filter divergence in practice [5,6,7]. The loosely-coupled SINS/GPS model is as follows: Correct navigation angular velocity. The loosely-coupled SINS/GPS navigation system’s error state vector is 15-dimensional [8]. The Sage-Husa adaptive algorithm cannot estimate both measurement noise covariance and process noise covariance matrix at the same time, because it will accumulate errors and result in filter divergence. HI is the measured model making up of the partial derivative of every state estimation value, which must be updated successively by recursion method
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