自适应惯性/天文器件级组合导航算法研究

Study of Automorphism INS/CNS Component Level Integrated Navigation Algorithm

在惯性/天文组合导航系统中,天文设备输出信息中所包含的噪声易受外界环境影响而发生变化,使得惯性/天文器件级组合导航数据融合精度受到制约。针对这一问题提出了基于自适应滤波的惯性/天文器件级数据融合算法,该算法在对导航参数误差、惯性器件误差进行滤波估计的同时,对天文量测信息噪声方差阵进行实时推算,从而优化滤波器对天文量测信息的处理,提高导航精度。经仿真试验可知,使用自适应滤波算法进行数据融合,导航位置精度可提高30%,具有理论价值与工程意义。

In the system of INS/CNS integrated navigation, the noise of data from CNS always change along with the environment＇s change, so that the result of navigation is not satisfied. In this thesis, we put forward automorphism INS/ CNS component level integrated navigation algorithm, which can compute the parameter of observation＇s noise as well as the errors of navigation and gyro. So that, the reformative algorithm effectively suppresses the various errors of INS. A series of tests show that automorphism INS/CNS component level integrated navigation algorithm can enhance the precision of ori- entation for 30%, thus the algorithm is significative for theory and application.