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.

中文翻译：

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具有低功耗微处理器的小型无人机的非线性姿态估计

在用于无人机姿态估计的传感器融合算法中，扩展卡尔曼滤波器 (EKF) 是最常用于估计的算法。在本文中，我们提出了一种新版本的 H2 估计，称为扩展 H2 估计，它可以克服扩展卡尔曼滤波器的局限性，特别是在计算速度、内存使用和均方根误差方面。我们制定了一种新的姿态估计算法，其中滤波器增益是在标称工作点附近离线设计的，但滤波器动力学是使用非线性系统动力学实现的。我们将 H2 最优估计器的这种实现称为扩展 H2 估计器。提出的解决方案在两种情况下进行了测试，分别对应于慢速和快速运动，