无人机组合导航直接法与间接法滤波方式比较

全球导航卫星系统/惯性导航系统（GNSS/INS）组合导航可以提供连续、高精度的位置、速度、姿态信息，被广泛应用于无人机的状态估计。其中滤波算法的构建是其组合关键。不同组合导航的模式会对导航定位结果产生相应的影响。针对直接法和间接法这2种常见的组合模式，分别构建了基于扩展卡尔曼滤波（EKF）的全球定位系统/惯性导航系统（GPS/INS）松组合模式，并将其运用于不同飞行场景下无人机（UAV）的实时动态状态估计。仿真场景以及实际数据验证结果表明，间接法在精度和稳定性方面优于直接法，直接法在滤波计算速率方面优于间接法。因此，当系统具有较高的计算性能，且面向高精度的应用情况下可选择间接法作为无人机导航的技术方案；对于快速求解但精度要求不高的应用情况下，选择直接法作为无人机导航的技术方案可以在一定程度上降低系统的成本。 

Comparison of direct and indirect filtering modes for UAV integrated navigation

Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated navigation that could provide continuous and high-accuracy position, velocity and attitude are widely used in UAV state estimation. Particularly, the design of filtering algorithm is the key to the integration. Besides, different modes of integrated navigation influence the navigation and positioning results. In this paper, the direct and indirect modes based Extended Kalman Filter (EKF) algorithms are designed for the loosely-coupled Glolal Positioning System/Inertial Navigation System (GPS/INS) integration in Unmanned Aerial Vehicle (UAV) state estimation and tested in different scenarios. The results of simulation and field test indicate that the integration algorithm with the indirect mode can provide higher accuracy and stability state estimation results than the direct mode but with an increased computational cost. It is recommended to choose the indirect mode based integration for the applications with higher accuracy and reliability, while for the applications with the requirements of lower accuracy and fast computation, the direct mode based integration is recommended to reduce the system cost.