Abstract
In order to use linear filtering algorithm, many linear Kalman filter models are based on linear hypothesis and assumption of small quantities. In order to improve the robustness of the Kalman filter algorithm in the initial alignment, the influence of the feedback coefficient on the initial alignment based on the state feedback Kalman filter algorithm is analyzed and the recommended values of feedback coefficients are given in this paper. In order to improve the accuracy of the measurement noise covariance matrix of Kalman filter, an improved algorithm based on adaptive and fading schemes for the matrix is proposed in this paper, and the matrix is diagonalized during the filtering process. The improved algorithms are verified by initial alignment simulation and turntable experiment, and the error precisions of misalignment angles are improved by one order of magnitude compared with traditional Kalman filter.
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