Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking

作     者：张祖涛 张家树 

作者机构：School of Mechanical Engineering Southwest Jiaotong University Sichuan Provincial Key Laboratory of Signal & Information Processing Southwest Jiaotong University 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2010年第19卷第10期

页      面：324-332页

核心收录：

学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：Project supported by the National Natural Science Foundation of China (Grant No. 60971104) the Fundamental Research Funds for the Cental Universities (Grant No. SWJTU09BR092) the Young Teacher Scientific Research Foundation of Southwest Jiaotong University (Grant No. 2009Q032) 

主　　题：unscented Kalman filter strong tracking filtering sampling strong tracking nonlinearunscented Kalman filter eye tracking 

摘      要：The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n＋ 2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions.