The Calman filter involves the process noise covariance matrix Q and measurement noise covariance matrix R, in the actual situation, Q and R are unknown or only know the approximate value.in the performance of the optimal filter factors should be considered in the error factors of Q and R, most of the algorithms can identify R well, but the identification of Q encountered the problem, in order to obtain more accurate output vector.An estimation method of Q and R, and the reliability of this method were proposed.At the same time, the output of A, E and R were predicted and expanded by using the extended Calman algorithm, and then data is processed to obtain the spacecraft trajectory coordinates.Finally, the algorithm results and comparative literature are present.Theoretical analysis and simulation experiments show that this algorithm can obtain better tracking accuracy and higher measurement accuracy, and verifies its validity and reliability.%针对卡尔曼滤波涉及到过程噪声协方差矩阵Q和量测噪声协方差矩阵R,实际情况中,Q和R未知或仅知道近似值,在最优滤波器的性能因素中需要考虑到Q和R的误差因素,大多数算法能够很好的辨识R,但在辨识Q时遇到了困难问题.为了能够更精确的获得输出矢量,提出了一种估计Q和R的方法,并对该方法的可靠性进行了实验仿真.同时采用扩展卡尔曼算法对输出的A、E和R进行预测和扩展,然后进行数据处理获得飞行器空间轨迹坐标,最后对计算的结果和文献对比.理论分析和实验仿真表明,算法能够获得更高的跟踪精度和更高的测量精度,验证了其有效性和可靠性.
展开▼
