论文标题
epoiriant滤波器（EQF）
Equivariant Filter (EqF)
论文作者
论文摘要
机器人技术，机电一体化和航空电子学中遇到的许多系统的运动学自然构成在均匀的空间上，也就是说，它们的状态在于配备了及时的谎言群体对称性的平滑歧管。本文提出了一种新颖的滤波器，即eproivariant滤波器（EQF），通过在对称组上摆姿势观察者状态，线性化的全局误差动力学从系统的等效性得出，并应用扩展的Kalman滤波器设计原理。我们表明，可以利用系统输出的均衡性来减少线性化误差并提高过滤器性能。示例应用程序的仿真实验表明，EQF明显胜过扩展的Kalman滤波器，并且减少的线性化误差会导致性能的明显改善。
The kinematics of many systems encountered in robotics, mechatronics, and avionics are naturally posed on homogeneous spaces, that is, their state lies in a smooth manifold equipped with a transitive Lie group symmetry. This paper proposes a novel filter, the Equivariant Filter (EqF), by posing the observer state on the symmetry group, linearising global error dynamics derived from the equivariance of the system, and applying extended Kalman filter design principles. We show that equivariance of the system output can be exploited to reduce linearisation error and improve filter performance. Simulation experiments of an example application show that the EqF significantly outperforms the extended Kalman filter and that the reduced linearisation error leads to a clear improvement in performance.
