对无人机导航状态进行在线估计时,一般采用简化的数学模型,因此需要采用自适应卡尔曼滤波。目前工程中应用广泛的几种自适应卡尔曼滤波存在各自缺点,对进一步提高无人机导航精度以及实际应用上有不利影响。利用残差变化率在不同飞行状态下的差异,选择估计窗的大小,以此给出一种简化的SAGE-HUSA卡尔曼滤波算法。仿真结果表明,该方法计算量小、思路清晰,适用于无人机导航的工程实践。

Self-adaptive Kalman filtering algorithm was adopted in the online estimate of navigation state of unmanned aerial vehicle (UAV) because the simplified model is often used. At the moment, the algorithms usually applied in this territory are not perfect. Take the advantage of residue characteristics and choose the estimation windows, a simplified SAGE-HUSA Kalman filtering algorithm was given. The result of simulation shows this method agrees with the demand of engineering application of UAV.