火箭弹道实时重建目前主要使用以位置速度为观测量的滤波算法,对外测测量数据来说,就需要首先将测距与测角齐备的数据转换为位置信息,从而不能将外测数据中的测距、测角、测速等各类信息同时进行有效利用。针对此情况,文中以泰勒展开式为变换基础,给出了一种将观测模型建立在测站地平坐标系下的自适应扩展卡尔曼滤波外弹道重建算法。并使用火箭某次飞行的实测数据对算法进行了验证,结果表明,文中滤波算法较有效地滤除了测量数据的噪声信息。

Kalman filters used in rocket ballistics' real-time computation are mainly based on observing model with position and velocity, which requires to convert the ranging and bearings data to position first in view of external ballistic metrical data. Therefore such algorithms can't take full advantage of range, bearings, and velocity measurement data in exterior trajectory measurement. In view of this, an adaptive Kalman filtering algorithm was proposed, which was based on the Taylor expressions and established observing model in station horizontal coordinate system. Then some real metrical data was used to test and verify it. The results indicate that this algorithm can filter noise effectively.