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
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
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
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
The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear...The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter(CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method.The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the thirdorder term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF(FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions.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 robust- ness of external in...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 robust- ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today＇s interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty in modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.展开更多
The paper analyzes the problem of blind source separation （BSS） based on the nonlinear principal component analysis （NPCA） criterion. An adaptive strong tracking filter （STF） based algorithm was developed, which...The paper analyzes the problem of blind source separation （BSS） based on the nonlinear principal component analysis （NPCA） criterion. An adaptive strong tracking filter （STF） based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.展开更多
同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLA...同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLAM性能下降。现有的概率假设密度(Probability Hypothesis Density,PHD)SLAM算法未考虑随机突变噪声,受到干扰时在线自适应调整能力较弱。为解决移动机器人因随机突变噪声导致状态估计和建图精度降低的问题,本文结合强跟踪滤波器(Strong Tracking Filter,STF)与PHD滤波器,提出了一种基于强跟踪的自适应PHD-SLAM滤波算法(Strong Tracking Probability Hypothesis Density Simultaneous Localization and Mapping,STPHD-SLAM)。该算法以PHD-SLAM为框架,针对过程噪声协方差和量测噪声协方差随机突变问题,本文通过在特征预测协方差中引入STF中的渐消因子,实现了对特征预测的自适应修正和卡尔曼增益的动态调整,从而增强了算法的自适应能力。其中渐消因子根据量测新息递归更新,确保噪声突变时每个时刻的量测新息保持正交,从而充分利用量测信息,准确并且快速地跟踪突变噪声。针对渐消因子激增导致的滤波器发散问题,本文对渐消因子进行边界约束,提高算法的鲁棒性。仿真结果表明,在量测噪声协方差和过程噪声协方差随机突变的情况下,所提算法相较于PHD-SLAM 1.0和PHD-SLAM 2.0的定位和建图精度都得到了提高,同时保证了计算效率。展开更多
It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel...It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter(STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate(K) and the moment(t).Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scrless than 1.761.展开更多
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. 61573283)
文摘The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter(CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method.The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the thirdorder term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF(FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions.
基金Supported by the National Natural Science Foundation of China (Grant No. 60572027)the Outstanding Young Researchers Foundation of Sichuan Province (Grant No. 03ZQ026-033)+1 种基金the Program for New Century Excellent Talents in University of China (Grant No. NCET-05-0794)the Young Teacher Foundation of Mechanical School (Grant No. MYF0806)
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 robust- ness of external interference and accuracy of eye tracking poses the primary obstacle to the integration of eye movements into today＇s interfaces. In this paper, we present a strong tracking finite-difference extended Kalman filter algorithm, aiming to overcome the difficulty in modeling nonlinear eye tracking. In filtering calculation, strong tracking factor is introduced to modify a priori covariance matrix and improve the accuracy of the filter. The filter uses finite-difference method to calculate partial derivatives of nonlinear functions for eye tracking. The latest experimental results show the validity of our method for eye tracking under realistic conditions.
基金Supported by the Basic Research Foundation of Tsinghua National Laboratory for Information Science and Technology (TNList) the National Natural Science Foundation of China (No. 60675002)
文摘The paper analyzes the problem of blind source separation （BSS） based on the nonlinear principal component analysis （NPCA） criterion. An adaptive strong tracking filter （STF） based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.
文摘同时定位与建图(Simultaneous Localization and Mapping,SLAM)技术使移动机器人在缺乏先验环境信息的条件下,能够在估计自身位姿的同时构建环境地图。然而,在海洋、矿洞等复杂环境中,移动机器人容易受到随机突变噪声的干扰,进而导致SLAM性能下降。现有的概率假设密度(Probability Hypothesis Density,PHD)SLAM算法未考虑随机突变噪声,受到干扰时在线自适应调整能力较弱。为解决移动机器人因随机突变噪声导致状态估计和建图精度降低的问题,本文结合强跟踪滤波器(Strong Tracking Filter,STF)与PHD滤波器,提出了一种基于强跟踪的自适应PHD-SLAM滤波算法(Strong Tracking Probability Hypothesis Density Simultaneous Localization and Mapping,STPHD-SLAM)。该算法以PHD-SLAM为框架,针对过程噪声协方差和量测噪声协方差随机突变问题,本文通过在特征预测协方差中引入STF中的渐消因子,实现了对特征预测的自适应修正和卡尔曼增益的动态调整,从而增强了算法的自适应能力。其中渐消因子根据量测新息递归更新,确保噪声突变时每个时刻的量测新息保持正交,从而充分利用量测信息,准确并且快速地跟踪突变噪声。针对渐消因子激增导致的滤波器发散问题,本文对渐消因子进行边界约束,提高算法的鲁棒性。仿真结果表明,在量测噪声协方差和过程噪声协方差随机突变的情况下,所提算法相较于PHD-SLAM 1.0和PHD-SLAM 2.0的定位和建图精度都得到了提高,同时保证了计算效率。
基金provided by the shale gas resource evaluation methods and exploration technology research project of the National Science and Technology Major Project of China(No.2016ZX05034)Graduate Innovative Engineering Funding Project of China University of Petroleum(East China)(No.YCX2021109)。
文摘It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter(STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate(K) and the moment(t).Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of Scrless than 1.761.
