Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estima...Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.展开更多
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
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 mu...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.展开更多
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance....To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.展开更多
In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even ped...In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.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
In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching th...In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.展开更多
电控空气悬架(electronically controlled air suspension,ECAS)系统的有效控制依赖于传感器实时采集的正确车身状态信号。针对电控空气悬架传感器卡死、恒偏差、恒增益3种故障,建立1种ECAS故障检测与隔离方法(fault detection and isol...电控空气悬架(electronically controlled air suspension,ECAS)系统的有效控制依赖于传感器实时采集的正确车身状态信号。针对电控空气悬架传感器卡死、恒偏差、恒增益3种故障,建立1种ECAS故障检测与隔离方法(fault detection and isolation,FDI)。建立电控空气悬架三自由度1/4车模型以及传感器故障时空气悬架模型,设计故障检测滤波器组,结合传感器实时测量值获得空气悬架输出残差,在此基础上确定故障检测指标,计算指标数值并选取适当阈值进行比较。诊断滤波器采用强跟踪滤波器方法进行设计,选取两级决策变量构造隔离决策函数,实现对故障传感器的检测与隔离。仿真分析表明,所提出的基于STF的方法实现了ECAS传感器故障的检测与隔离,有效提高了车辆控制的可靠性与安全性。展开更多
文摘Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules.Because the health parameters are unmeasurable,researchers estimate them only based on the available measurement parameters.Kalman filter-based approaches are the most commonly used estimation approaches;how-ever,the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty,and their ability to track the mutation condition is influenced by historical data.Therefore,in this paper,an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter(STEKF)approach is proposed to estimate the gas turbine health parameters.The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches.The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter(EKF)and the unscented Kalman filter(UKF).The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.
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)
文摘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.
文摘To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
基金funded by the project“Design of System Integration Construction Scheme Based on Functions of Each Module” (No.XDHT2020169A)the project“Development of Indoor Inspection Robot System for Substation” (No.XDHT2019501A).
文摘In recent years,a number of wireless indoor positioning(WIP),such as Bluetooth,Wi-Fi,and Ultra-Wideband(UWB)technologies,are emerging.However,the indoor environment is complex and changeable.Walls,pillars,and even pedestrians may block wireless signals and produce non-line-of-sight(NLOS)deviations,resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning.This work proposed a strong tracking particle filter based on the chi-square test(SPFC)for indoor positioning.SPFC can fuse indoor wireless signals and the information of the inertial sensing unit(IMU)in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result.Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1%and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.
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).
基金National Natural Science Foundation of China !( No.69772 0 3 1)
文摘In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.
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。
文摘电控空气悬架(electronically controlled air suspension,ECAS)系统的有效控制依赖于传感器实时采集的正确车身状态信号。针对电控空气悬架传感器卡死、恒偏差、恒增益3种故障,建立1种ECAS故障检测与隔离方法(fault detection and isolation,FDI)。建立电控空气悬架三自由度1/4车模型以及传感器故障时空气悬架模型,设计故障检测滤波器组,结合传感器实时测量值获得空气悬架输出残差,在此基础上确定故障检测指标,计算指标数值并选取适当阈值进行比较。诊断滤波器采用强跟踪滤波器方法进行设计,选取两级决策变量构造隔离决策函数,实现对故障传感器的检测与隔离。仿真分析表明,所提出的基于STF的方法实现了ECAS传感器故障的检测与隔离,有效提高了车辆控制的可靠性与安全性。
