An Improved Adaptive Kalman Filter for In-motion Initial Alignment of GPS-Aided SINS

In GPS-aided strap-down inertial navigation system, in-motion initial alignment is crucial and can be solved with a closed-loop scheme based on state estimation. With this method, the noise covariance matrices need to be estimated, which, however, can be inaccurate in practice. In this paper, a novel adaptive Kalman filter is proposed to address the above problem. The state and measurement noise covariance matrices are jointly estimated based on a variational Bayesian approach, in which the prior and posterior probability density functions of the state noise covariance matrix and one-step prediction error covariance matrix are assumed to have the same form. Simulation results demonstrate that the proposed algorithm can improve the initial alignment accuracy of the in-motion initial alignment based on a closed-loop scheme as compared with an existing baseline adaptive Kalman filter