A Real-Time Adaptive High-Gain EKF, Applied to a Quadcopter Inertial Navigation System
A Real-Time Adaptive High-Gain EKF, Applied to a Quadcopter Inertial Navigation System
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DOI：
10.1109/tie.2013.2253063
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发表时间：
2014
影响因子：
7.7
通讯作者：
K. Sebesta;Nicolas Boizot
中科院分区：
文献类型：
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作者：
K. Sebesta;Nicolas Boizot
作者演示了自适应高增益扩展卡尔曼滤波器 (EKF) (AEKF) 在四轴飞行器无人机 (UAV) 上的实际应用。 AEKF 在状态估计方面具有多种优势，因为它结合了良好的滤波特性和对大扰动的更高敏感性。它通过根据称为创新的指标改变高增益参数来实现这一点。与许多自适应观测器不同，AEKF 经数学证明可以全局收敛，在考虑鲁棒控制时，这比传统 EKF 具有显着优势。 AEKF 在无人机的惯性导航系统 (INS) 上实现。当传感器噪声较大且受到限制时，完整的 INS 可能会出现问题，特别是在四轴飞行器等高度动态不稳定的系统的情况下。仿真和实验数据表明AEKF适合该惯导系统。
The authors demonstrate the practical application of the adaptive high-gain extended Kalman filter (EKF) (AEKF) onboard a quadcopter unmanned aerial vehicle (UAV). The AEKF presents several advantages in state estimation, as it combines good filtering properties with an increased sensitivity to large perturbations. It does this by varying the high-gain parameter according to a metric called innovation. Unlike many adaptive observers, the AEKF is mathematically proven to globally converge, a significant advantage over the traditional EKF when considering robust controls. The AEKF is implemented on the UAV's inertial navigation system (INS). Full INSs can have problems when sensors are noisy and limited, particularly in the case of highly dynamically unstable systems such as a quadcopter. Simulation and experimental data show that the AEKF is suitable for this INS.