自适应卡尔曼滤波在SINS静基座初始对准中的应用

Application of adaptive Kalman filter in initial alignment of SINS on stationary base

由于捷联惯导系统模型参数往往与实际的物理过程具有一定的偏差,并且系统的噪声与量测噪声的统计特性不是精确已知的,因此采用常规的卡尔曼滤波算法不能获得理想的滤波效果。为避免滤波发散,采用Sage-Husa自适应滤波算法应用于捷联惯导初始对准,仿真结果表明该方法相对于卡尔曼滤波方法在收敛精度和速度上都有很大的改进。

Since the parameters of a Strap-down Inertial Navigation System（SINS） model usually have some deviations from the actual physical process, and the noise statistic characteristics is not exactly known, the normal Kalman filter may behave badly or diverge. In order to avoid faltering divergence, the Sage-Husa adaptive filtering algorithm is applied to the initial alignment of SINS. The simulation result showed that the algorithm improves the precision and the speed of convergence to a great degree.