A Real-Time Adaptive High-Gain EKF, Applied to a Quadcopter Inertial Navigation System

摘要

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.