论文标题
低功率微处理器的小型无人机的非线性态度估计
Nonlinear Attitude Estimation for Small UAVs with Low Power Microprocessors
论文作者
论文摘要
在用于传感器融合的算法中，用于无人飞机的态度估计，扩展的卡尔曼滤波器（EKF）是最常用的估计。在本文中，我们提出了一个新版本的H2估计，称为扩展H2估计，该估计可以克服扩展的Kalman滤波器的局限性，特别是在计算速度，内存使用情况和根平方误差方面。我们制定了一种新的态度估计算法，其中滤波器增益是围绕名义操作点的离线设计的，但是使用非线性系统动力学实现了滤波器动力学。我们将H2最佳估计器的实现称为扩展的H2估计器。在两种情况下测试了介绍的解决方案，对应于缓慢和快速运动，并与上述性能指标中的EKF进行了比较。
Among algorithms used for sensor fusion for attitude estimation in unmanned aerial vehicles, the Extended Kalman Filter (EKF) is the most commonly used for estimation. In this paper, we propose a new version of H2 estimation called extended H2 estimation that can overcome the limitations of the extended Kalman Filter, specifically with respect to computational speed, memory usage, and root mean squared error. We formulate a new attitude-estimation algorithm, where the filter gain is designed offline about a nominal operating point, but the filter dynamics is implemented using the nonlinear system dynamics. We refer to this implementation of the H2 optimal estimator as the extended H2 estimator. The solution presented is tested on two cases, corresponding to slow and rapid motions, and compared against the EKF in the performance metrics mentioned above.
