부산대학교 도서관

In many cases, outliers are contained in sensor signals, and these deteriorate performances of control systems, e.g., UAV and UGV using non-contact sensors. Many reduction methods of the outliers have been proposed. One of the methods is robust Kalman filter (RKF) via ℓ 1 regression. The method is easy to implement and compute due to a simple structure and convex optimization problem, so the method attracts many attentions. However, parameters of the method are designed by heuristic methods. In this paper, we propose a design method of RKF via ℓ 1 regression. We show that statistics of Gaussian noise determine the parameters of RKF, and we can design the parameters systematically. Then, we apply the method to a velocity estimation and control of a two-wheeled vehicle with outliers. Effectiveness is demonstrated by some numerical simulations.