无人机数据采集EKF滤波悬停稳定的研究与应用

许多国内外的研究机构致力于四旋翼无人机飞行控制姿态和高度悬停稳定的研究,以实现四旋翼无人机的自主飞行。四旋翼无人机是一种多输入、强耦合、多变量、欠驱动的系统,其稳定性、数据传输的可靠性、精确度、实时性对飞机性能起着决定性的作用。针对四轴无人机悬停运动测试环节中不稳定的影响,基于已有的无人机平台,最新科研前沿的文献,以ARM嵌入式系统作为上位机,设计了一个无人机数据采集的扩展卡尔曼滤波器(EKF),结合二次型最优控制预估气压计最优初始矩阵值进行开源编程。从扩展卡尔曼滤波器的建立与优化,原程序气压计程序严谨的探讨、衔接和写入飞控,最终在软件匿名科创地面站上,通过无人机对气压计大量实时悬停数据采集,实验仿真进行滤波图形对比验证和无人机实际运行的稳定性分析表明,提出的无人机数据采集扩展卡尔曼滤波方法效果良好,具有良好的应用和推广价值。

Research and Application of EKF Filter Hover Stability in UAV Data Acquisition

Many domestic and foreign research institutions are committed to the four-rotor UAV flight control attitude and high hover stability research, in order to achieve the four-rotor UAV"s autonomous flight. The four-rotor unmanned aerial vehicle (UAV) is a multi-input, strong coupling, multivariable, underactuated system. Its stability, data transmission reliability, accuracy and real-time performance play a decisive role in aircraft performance. According to the existing UAV platform, the latest research frontier literature, ARM embedded system as the host computer, designed a UAV data acquisition for the four-axis UAV hover movement test link instability in the existing Extended Kalman filter (EKF), combined with quadratic optimal control to predict the barometer optimal initial matrix value for open source programming. From the expansion of the Kalman filter to establish and optimize the original program barometer program rigorous discussion, convergence and write flight control, and ultimately in the software anonymous Kechuang ground station, through the UAV on the barometer large number of real-time hovering data acquisition The experimental results show that the proposed method of UAV data acquisition and expansion Kalman filtering is effective and has good application and popularization value.