摘要
针对无人机着陆过程中,可用引导源多、导航精度要求高以及传感器的引导精度时变等问题,提出了基于多传感器的自适应融合导航技术。在数据预处理方面,采用内插外推法与基于Savitzky-Golay滤波的信号平滑方法分别实现时间配准与野点平滑。其后重点研究基于自适应窗长的动态方差估计融合算法,对多传感器引导信息进行融合。首先,采用基于自适应时间窗的残差计算方法对各传感器的量测误差方差进行动态估计,根据传感器量测噪声的变化设置不同的时间窗长,以提高对传感器量测误差方差估计的准确性。其次,根据各信息源实时残差方差的比值调整融合权重分配,实现融合算法的自适应调整。最后,针对典型的着陆场景,通过项目合作单位提供的半物理仿真数据对融合算法进行仿真验证。
The autonomous landing technology of drones is a focus of UAV development.Therefore,aiming at the problems of a large amount of available guidance sources,high requirements for navigation accuracy,and time-varying guidance accuracy of the sensor during the landing process,this paper proposes an adaptive fusion navigation technol-ogy based on multiple sensors.In terms of data preprocessing,the interpolation and extrapolation method is used to achieve time registration and the signal smoothing method based on Savitzky-Golay filtering is used to smooth the sig-nal outliers.Then we focus on a dynamic variance estimation fusion algorithm based on adaptive window length to fuse multi-sensor guidance information.Firstly,the residual variance calculation method based on the adaptive time window is used to estimate the measurement error variance of each sensor dynamically,and different time windows are set ac-cording to the change of measurement noise in sensor data to improve the accuracy of the measurements error variance estimation.Secondly,according to the ratio of real-time residual variance of each information source,the fusion weight distribution is adjusted to realize the adaptive adjustment of fusion algorithm.Finally,aiming at the typical landing veri-fication scenario,the fusion algorithm is simulated and verified by the semi-physical simulation data provided by the project partner.
作者
时莎莎
韩昕怡
涂阔
李一凡
胡劲文
SHI Shasha;HAN Xinyi;TU Kuo;LI Yifan;HU Jinwen(China Air-to-Air Missile Research Institute,Luoyang 471009,China;School of Automation,Northwestern Polytechnical University,Xi’an 710129,China)
出处
《无人系统技术》
2021年第5期31-41,共11页
Unmanned Systems Technology
基金
国家自然科学基金项目(61803309)
中央高校基本科研业务费资助项目(3102019ZDHKY02)
陕西省重点研发计划项目(2020ZDLGY06-02)
中国博士后科学基金(2018M633574)
航空基金(2019ZA053008)。