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.

为了使用线性滤波算法，许多线性卡尔曼滤波模型都基于线性假设和少量假设。为了提高卡尔曼滤波算法在初始对准中的鲁棒性，分析了基于状态反馈卡尔曼滤波算法的反馈系数对初始对准的影响，并给出了反馈系数的推荐值。为了提高卡尔曼滤波器的测量噪声协方差矩阵的准确性，提出了一种基于自适应和衰落方案的改进算法，对矩阵进行滤波。通过初始对准仿真和转台实验对改进算法进行了验证，