The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi...The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.展开更多
[目的/意义]针对温室温湿度预测中多传感器数据融合可靠性低、传统模型忽略温湿度动态耦合,以及参数调优依赖人工经验等问题。[方法]首先,对传统卡尔曼(Kalman)滤波算法实施改进,通过动态调整过程噪声协方差和观测噪声协方差,结合新息...[目的/意义]针对温室温湿度预测中多传感器数据融合可靠性低、传统模型忽略温湿度动态耦合,以及参数调优依赖人工经验等问题。[方法]首先,对传统卡尔曼(Kalman)滤波算法实施改进,通过动态调整过程噪声协方差和观测噪声协方差,结合新息方差动态分配多传感器权重。其次,针对温湿度的强耦合性及其协同控制的需求,构建多输出长短期记忆-注意力机制(Long Short-Term Memory-Attention,LSTM-Attention)模型,以温湿度协同预测为目标,引入注意力机制自适应加权关键环境因子,并采用灰狼优化算法(Grey Wolf Optimizer,GWO)自动对超参数进行寻优。[结果和讨论]提出的自适应卡尔曼滤波算法在多点温湿度融合中的平均绝对偏差分别为1.59℃和8.64%,比传统卡尔曼滤波算法分别降低1.24%、8.57%。以该算法融合结果作为模型训练集,模型在温湿度预测中决定系数R2分别达到98.2%和99.3%,比传统Kalman提升4.7%和4.3%。GWO-LSTM-Atten⁃tion模型的温湿度预测均方根误差分别为0.7768℃和2.0564%,比LSTM、LSTM-Attention时间序列预测模型分别降低15.6%、6.6%,湿度分别降低29.2%、5.7%。[结论]提出的自适应卡尔曼融合算法能够有效抑制异常值影响,可在非平稳环境变化下实现多传感器数据可靠融合。在温室多环境因子预测中,GWO-LSTM-Attention模型温湿度预测值在未来可作为控制温室环境的重要参考,进而实现对温室环境的实时调控。展开更多
Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalma...Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalman filtering algorithm to determine the position of the target centroid and subsequently adjust the current search window adaptively according to the target centroid position and the previous frame search window boundary.The derived search window is more closely matched to the location of the target,which improves the accuracy and reliability of tracking.The environmental influence and other influencing factors on the algorithm are also reduced.Through comparison and analysis of the experiments,the modified algorithm demonstrates good stability and adaptability,and can effectively solve the problem of large area occlusion and similar interference.展开更多
Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcomin...Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.展开更多
In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate r...In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.展开更多
The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of ...The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.展开更多
基金supported in part by Sichuan Science and Technology Program under Grant No.2025ZNSFSC151in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27030201+1 种基金the Natural Science Foundation of China under Grant No.U21B6001in part by the Natural Science Foundation of Tianjin under Grant No.24JCQNJC01930.
文摘The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.
文摘[目的/意义]针对温室温湿度预测中多传感器数据融合可靠性低、传统模型忽略温湿度动态耦合,以及参数调优依赖人工经验等问题。[方法]首先,对传统卡尔曼(Kalman)滤波算法实施改进,通过动态调整过程噪声协方差和观测噪声协方差,结合新息方差动态分配多传感器权重。其次,针对温湿度的强耦合性及其协同控制的需求,构建多输出长短期记忆-注意力机制(Long Short-Term Memory-Attention,LSTM-Attention)模型,以温湿度协同预测为目标,引入注意力机制自适应加权关键环境因子,并采用灰狼优化算法(Grey Wolf Optimizer,GWO)自动对超参数进行寻优。[结果和讨论]提出的自适应卡尔曼滤波算法在多点温湿度融合中的平均绝对偏差分别为1.59℃和8.64%,比传统卡尔曼滤波算法分别降低1.24%、8.57%。以该算法融合结果作为模型训练集,模型在温湿度预测中决定系数R2分别达到98.2%和99.3%,比传统Kalman提升4.7%和4.3%。GWO-LSTM-Atten⁃tion模型的温湿度预测均方根误差分别为0.7768℃和2.0564%,比LSTM、LSTM-Attention时间序列预测模型分别降低15.6%、6.6%,湿度分别降低29.2%、5.7%。[结论]提出的自适应卡尔曼融合算法能够有效抑制异常值影响,可在非平稳环境变化下实现多传感器数据可靠融合。在温室多环境因子预测中,GWO-LSTM-Attention模型温湿度预测值在未来可作为控制温室环境的重要参考,进而实现对温室环境的实时调控。
基金Supported by the Scholarship of China Scholarship Council(CSC)(201606935043)
文摘Directed at the problem of occlusion in target tracking,a new improved algorithm based on the Meanshift algorithm and Kalman filter is proposed.The algorithm effectively combines the Meanshift algorithm with the Kalman filtering algorithm to determine the position of the target centroid and subsequently adjust the current search window adaptively according to the target centroid position and the previous frame search window boundary.The derived search window is more closely matched to the location of the target,which improves the accuracy and reliability of tracking.The environmental influence and other influencing factors on the algorithm are also reduced.Through comparison and analysis of the experiments,the modified algorithm demonstrates good stability and adaptability,and can effectively solve the problem of large area occlusion and similar interference.
文摘Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.
基金Supported by the National Natural Science Foundation of China （50979017, NSFC60775060） the National High Technology Ship Research Project of China （GJCB09001）
文摘In the normal operation condition, a conventional square-root cubature Kalman filter （SRCKF） gives sufficiently good estimation results. However, if the measurements are not reliable, the SRCKF may give inaccurate results and diverges by time. This study introduces an adaptive SRCKF algorithm with the filter gain correction for the case of measurement malfunctions. By proposing a switching criterion, an optimal filter is selected from the adaptive and conventional SRCKF according to the measurement quality. A subsystem soft fault detection algorithm is built with the filter residual. Utilizing a clear subsystem fault coefficient, the faulty subsystem is isolated as a result of the system reconstruction. In order to improve the performance of the multi-sensor system, a hybrid fusion algorithm is presented based on the adaptive SRCKF. The state and error covariance matrix are also predicted by the priori fusion estimates, and are updated by the predicted and estimated information of subsystems. The proposed algorithms were applied to the vessel dynamic positioning system simulation. They were compared with normal SRCKF and local estimation weighted fusion algorithm. The simulation results show that the presented adaptive SRCKF improves the robustness of subsystem filtering, and the hybrid fusion algorithm has the better performance. The simulation verifies the effectiveness of the proposed algorithms.
文摘The standalone Global Positioning System (GPS) does not meet the higher accuracy requirements needed for approach and landing phase of an aircraft. To meet the Category-I Precision Approach (CAT-I PA) requirements of civil aviation, satellite based augmentation system (SBAS) has been planned by various countries including USA, Europe, Japan and India. The Indian SBAS is named as GPS Aided Geo Augmented Navigation (GAGAN). The GAGAN network consists of several dual frequency GPS receivers located at various airports around the Indian subcontinent. The ionospheric delay, which is a function of the total electron content (TEC), is one of the main sources of error affecting GPS/SBAS accuracy. A dual frequency GPS receiver can be used to estimate the TEC. However, line-of-sight TEC derived from dual frequency GPS data is corrupted by the instrumental biases of the GPS receiver and satellites. The estimation of receiver instrumental bias is particularly important for obtaining accurate estimates of ionospheric delay. In this paper, two prominent techniques based on Kalman filter and Self-Calibration Of pseudo Range Error (SCORE) algorithm are used for estimation of instrumental biases. The estimated instrumental bias and TEC results for the GPS Aided Geo Augmented Navigation (GAGAN) station at Hyderabad (78.47°E, 17.45°N), India are presented.
