Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction.However,due to the high nonlinearity of eye motion,how to ensure the robustness of external interfe...Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction.However,due to the high nonlinearity of eye motion,how to ensure the robustness of external interference and accuracy of eye tracking pose the primary obstacle to the integration of eye movements into today＇s interfaces.In this paper,we present a strong tracking unscented Kalman filter （ST-UKF） algorithm,aiming to overcome the difficulty in nonlinear eye tracking.In the proposed ST-UKF,the Suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking.Compared with the related Kalman filter for eye tracking,the proposed ST-UKF has potential advantages in robustness and tracking accuracy.The last experimental results show the validity of our method for eye tracking under realistic conditions.展开更多
To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is intro...To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).展开更多
Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter.In this paper,we study the design of nonlinear filters based on SCKF for the system with one s...Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter.In this paper,we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change.First,we give the SCKF that deals with the one step correlation between process and measurement noises,SCKF-CN in short.Second,we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor,which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change.Accordingly,the tracking performance of SCKF is greatly improved.A universal nonlinear estimator is proposed,which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises,but also achieve an excellent strong tracking performance towards the abrupt change of target state.Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.展开更多
基金supported by the National Natural Science Foundation of China(No.60971104)the Program for New Century Excellent Talents in University of China(No.NCET-05-0794)the Young Teacher Scientific Research Foundation of Southwest Jiaotong University(No.2009Q032)
文摘Non-intrusive methods for eye tracking are important for many applications of vision-based human computer interaction.However,due to the high nonlinearity of eye motion,how to ensure the robustness of external interference and accuracy of eye tracking pose the primary obstacle to the integration of eye movements into today＇s interfaces.In this paper,we present a strong tracking unscented Kalman filter （ST-UKF） algorithm,aiming to overcome the difficulty in nonlinear eye tracking.In the proposed ST-UKF,the Suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking.Compared with the related Kalman filter for eye tracking,the proposed ST-UKF has potential advantages in robustness and tracking accuracy.The last experimental results show the validity of our method for eye tracking under realistic conditions.
基金National Natural Science Foundations of China(Nos.51175082,60874092,51375088)
文摘To solve the problem that the choice of softening factor in conventional adaptive strong tracking filter( STF) greatly relies on the experience and computer simulation,a new concept of softening factor matrix is introduced and a fuzzy adaptive strong tracking cubature Kalman filter( FASTCKF) based on fuzzy logic controller is proposed. This method monitors residual absolute mean and standard deviation of each measurement component with fuzzy logic adaptive controller( FLAC),and adjusts the softening factor matrix dynamically by fuzzy rules,which is capable to modify suboptimal fading factor of STF adaptively and improve the filter's robust adaptive capacity. The simulation results show that the improved filtering performance is superior to the conventional square root cubature Kalman filter( SCKF) and the strong tracking square root cubature Kalman filter( STSCKF).
文摘电池组中单体间存在的不一致性是电池状态估计问题中的一大难点。针对串联锂离子电池组,提出了一种基于强跟踪滤波器（strong tracking filter,STF）与LevenbergMarquardt（LM）算法相结合的电池组不一致性辨识与状态估计的新方法。首先针对＂参考单体＂给出了一阶等效电路模型与开路电压–荷电状态（state of charge,SOC）特性关系曲线,通过STF算法得到其状态估计与参数估计;其次建立不同单体的＂电压相似函数＂,并引入LM算法对SOC、极化电压、欧姆内阻3种不一致因素进行辨识;最后对2组5个LiFePO4单体串联的电池组在不同的工况下进行了实验验证。结果表明,所提方法对各单体的状态与内阻估计误差在合理的范围内,对电池组不一致性辨识与状态估计具有良好的效果。
基金supported by the National Natural Science Foundation of China (Nos.60934009,60804064,and 30800248)the China Post-doctoral Science Foundation (No.20100471727)the Science and Technology Department of Zhejiang Province,China (No.2009C34016)
文摘Square-root cubature Kalman filter (SCKF) is more effective for nonlinear state estimation than an unscented Kalman filter.In this paper,we study the design of nonlinear filters based on SCKF for the system with one step noise correlation and abrupt state change.First,we give the SCKF that deals with the one step correlation between process and measurement noises,SCKF-CN in short.Second,we introduce the idea of a strong tracking filter to construct the adaptive square-root factor of the prediction error covariance with a fading factor,which makes SCKF-CN obtain outstanding tracking performance to the system with target maneuver or abrupt state change.Accordingly,the tracking performance of SCKF is greatly improved.A universal nonlinear estimator is proposed,which can not only deal with the conventional nonlinear filter problem with high dimensionality and correlated noises,but also achieve an excellent strong tracking performance towards the abrupt change of target state.Three simulation examples with a bearings-only tracking system are illustrated to verify the efficiency of the proposed algorithms.
