To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satell...To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.展开更多
MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The Kalman filter assumes zero-mean Gaussian process a...MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The Kalman filter assumes zero-mean Gaussian process and measurement noise variables,and then recursively computes optimal state estimates.However,establishing the exact noise statistics is a non-trivial task.Additionally,this noise often varies widely in operation.Addressing this challenge is the focus of adaptive Kalman filtering techniques.In the covariance scaling method,the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating window.This study proposes a new approach where individual elements of Q and R are scaled element-wise to ensure more granular adaptation of noise components and hence improve accuracy.In addition,the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady state.Experimental results show that the root mean square errors for both pith and roll axes are significantly reduced compared to the conventional noise adaptation method,albeit at a slightly higher computational cost.Specifically,the root mean square pitch errors are 1.1∘under acceleration and 2.1∘under rotation,which are significantly less than the corresponding errors of the adaptive complementary filter and conventional covariance scaling-based adaptive Kalman filter tested under the same conditions.展开更多
Vertical tire forces are essential for vehicle modelling and dynamic control.However,an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish.The current methods require a large amoun...Vertical tire forces are essential for vehicle modelling and dynamic control.However,an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish.The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint.To simplify the design process and reduce the demand of the sensors,this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control.The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter(ATEKF).To adapt to the widely varying parameters,a sliding mode update is designed to make the ATEKF adaptive,and together with the use of an initial setting update and a vertical tire force adjustment,the overall system becomes more robust.In particular,the model aims to eliminate the effects of the over-constraint and the uneven weight distribution.The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation,and its performance is better than that of the treble extend Kalman filter.展开更多
This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fadi...This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fading Kalman filter(AFKF)is designed for estimating the state of the smart grid.The AFKF can completely filter out the Gaussian noise of the power system,and obtain a more accurate state change curve(including consideration of the attack).A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs.Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity,especially for very weak D-LAA attacks.Finally,the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.展开更多
Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly convergin...Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.展开更多
Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digit...Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digital-filter-based improved adaptive Kalman method is proposed to improve accuracy and reliability of the altitude measurement information.A unique sensor data fusion structure is designed to make different sensors switch automatically in different environment.Simulation and experimental results show that an improved Sage-Husa adaptive extended Kalman filter(SHAEKF) is adopted in altitude data fusion which means that altitude error is limited to 1.5m in high altitude and 1.2m near the ground.This method is proved feasible and effective through hovering flight test and three-dimensional track flight experiment.展开更多
Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced ...Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive Kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.展开更多
Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to...Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight(NLOS)propagation.In this paper,we propose a passive anchor assisted localization(PAAL)scheme,where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent.The proposed method fully exploits the time-difference-of-arrival(TDOA)information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization.The performance limits of the PAAL system are derived as a benchmark via the information inequality.Moreover,we implement the PAAL system on a low-cost UWB platform,which can achieve 20 cm localization accuracy in NLOS environments.展开更多
Measuring eye movement is a fundamental approach in cognitive science as it provides a variety of insightful parameters that reflect brain states such as visual attention and emotions.Combining eye-tracking with multi...Measuring eye movement is a fundamental approach in cognitive science as it provides a variety of insightful parameters that reflect brain states such as visual attention and emotions.Combining eye-tracking with multimodal neural recordings or manipulation techniques is beneficial for understanding the neural substrates of cognitive function.Many commercially-available and custom-built systems have been widely applied to awake,head-fixed small animals.However,the existing eyetracking systems used in freely-moving animals are still limited in terms of their compatibility with other devices and of the algorithm used to detect eye movements.Here,we report a novel system that integrates a general-purpose,easily compatible eye-tracking hardware with a robust eye feature-detection algorithm.With ultra-light hardware and a detachable design,the system allows for more implants to be added to the animal's exposed head and has a precise synchronization module to coordinate with other neural implants.Moreover,we systematically compared the performance of existing commonly-used pupil-detection approaches,and demonstrated that the proposed adaptive pupil feature-detection algorithm allows the analysis of more complex and dynamic eye-tracking data in freemoving animals.Synchronized eye-tracking and electroencephalogram recordings,as well as algorithm validation under five noise conditions,suggested that our system is flexibly adaptable and can be combined with a wide range of neural manipulation and recording technologies.展开更多
基金supported in part by the Shandong Natural Science Foundation under Grant ZR2020MF067.
文摘To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.
文摘MEMS(micro-electro-mechanical-system)IMU(inertial measurement unit)sensors are characteristically noisy and this presents a serious problem to their effective use.The Kalman filter assumes zero-mean Gaussian process and measurement noise variables,and then recursively computes optimal state estimates.However,establishing the exact noise statistics is a non-trivial task.Additionally,this noise often varies widely in operation.Addressing this challenge is the focus of adaptive Kalman filtering techniques.In the covariance scaling method,the process and measurement noise covariance matrices Q and R are uniformly scaled by a scalar-quantity attenuating window.This study proposes a new approach where individual elements of Q and R are scaled element-wise to ensure more granular adaptation of noise components and hence improve accuracy.In addition,the scaling is performed over a smoothly decreasing window to balance aggressiveness of response and stability in steady state.Experimental results show that the root mean square errors for both pith and roll axes are significantly reduced compared to the conventional noise adaptation method,albeit at a slightly higher computational cost.Specifically,the root mean square pitch errors are 1.1∘under acceleration and 2.1∘under rotation,which are significantly less than the corresponding errors of the adaptive complementary filter and conventional covariance scaling-based adaptive Kalman filter tested under the same conditions.
基金Supported by Basic and Applied Basic Research Foundation of Guangdong Province of China(Grant No.2019A1515110763).
文摘Vertical tire forces are essential for vehicle modelling and dynamic control.However,an evaluation of the vertical tire forces on a multi-axle truck is difficult to accomplish.The current methods require a large amount of experimental data and many sensors owing to the wide variation of the parameters and the over-constraint.To simplify the design process and reduce the demand of the sensors,this paper presents a practical approach to estimating the vertical tire forces of a multi-axle truck for dynamic control.The estimation system is based on a novel vertical force model and a proposed adaptive treble extend Kalman filter(ATEKF).To adapt to the widely varying parameters,a sliding mode update is designed to make the ATEKF adaptive,and together with the use of an initial setting update and a vertical tire force adjustment,the overall system becomes more robust.In particular,the model aims to eliminate the effects of the over-constraint and the uneven weight distribution.The results show that the ATEKF method achieves an excellent performance in a vertical force evaluation,and its performance is better than that of the treble extend Kalman filter.
基金the Science and Technology Project of the State Grid Shandong Electric Power Company:Research on the vulnerability and prevention of the electrical cyber-physical monitoring system based on interdependent networksthe National Natural Science Foundation of China(61873057)and the Education Department of Jilin Province(JJKH20200118KJ).
文摘This paper presents an effective and feasible method for detecting dynamic load-altering attacks(D-LAAs)in a smart grid.First,a smart grid discrete system model is established in view of D-LAAs.Second,an adaptive fading Kalman filter(AFKF)is designed for estimating the state of the smart grid.The AFKF can completely filter out the Gaussian noise of the power system,and obtain a more accurate state change curve(including consideration of the attack).A Euclidean distance ratio detection algorithm based on the AFKF is proposed for detecting D-LAAs.Amplifying imperceptible D-LAAs through the new Euclidean distance ratio improves the D-LAA detection sensitivity,especially for very weak D-LAA attacks.Finally,the feasibility and effectiveness of the Euclidean distance ratio detection algorithm are verified based on simulations.
基金supported by Natural Science Foundation Research Project of Shanxi Science and Technology Department(2016JM1032)
文摘Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.
基金Supported by the National Natural Science Foundation of China(No.61304017,11372309)Key Technology Development Project of Jilin Province(No.20150204074GX)+1 种基金the Project Development Plan of Science and Technology(No.20150520111zh)the Provincial Special Funds Project of Science and Technology Cooperation(No.2014SYHZ0004)
文摘Altitude regulation is a fundamental problem in UAV(unmanned aerial vehicles) control to ensure hovering and autonomous navigation performance.However,data from altitude sensors may be unstable by interference.A digital-filter-based improved adaptive Kalman method is proposed to improve accuracy and reliability of the altitude measurement information.A unique sensor data fusion structure is designed to make different sensors switch automatically in different environment.Simulation and experimental results show that an improved Sage-Husa adaptive extended Kalman filter(SHAEKF) is adopted in altitude data fusion which means that altitude error is limited to 1.5m in high altitude and 1.2m near the ground.This method is proved feasible and effective through hovering flight test and three-dimensional track flight experiment.
基金National Natural Science Foundation of China(Grant Nos.52072072,52025121 and 51605087).
文摘Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive Kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.
文摘Ultra-Wide Bandwidth(UWB)localization based on time of arrival(TOA)and angle of arrival(AOA)has attracted increasing interest owing to its high accuracy and low cost.However,existing localization methods often fail to achieve satisfactory accuracy in realistic environments due to multipath effects and non-line-of-sight(NLOS)propagation.In this paper,we propose a passive anchor assisted localization(PAAL)scheme,where the active anchor obtains TOA/AOA measurements to the agent while the passive anchors capture the signals from the active anchor and agent.The proposed method fully exploits the time-difference-of-arrival(TDOA)information from the measurements at the passive anchors to complement single-anchor joint TOA/AOA localization.The performance limits of the PAAL system are derived as a benchmark via the information inequality.Moreover,we implement the PAAL system on a low-cost UWB platform,which can achieve 20 cm localization accuracy in NLOS environments.
基金supported in part by the National Key R&D Program of China(2021ZD0203902 and 2018YFA0701403)the Key Area R&D Program of Guangdong Province(2018B030338001 and 2018B030331001)+9 种基金the National Natural Science Foundation of China(31500861,31630031,91732304,and 31930047)the Chang Jiang Scholars Program and the Ten Thousand Talent Program,the International Big Science Program Cultivating Project of the Chinese Academy of Science(CAS)(172644KYS820170004)the Strategic Priority Research Program of the CAS(XDB32030100)the Youth Innovation Promo-tion Association of the CAS(2017413)Shenzhen Government Basic Research Grants(JCYJ20170411140807570,JCYJ20170413164535041)the Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20160429185235132)a Helmholtz-CAS joint research grant(GJHZ1508)the Guangdong Provincial Key Laboratory of Brain Connectome and Behavior(2017B030301017)the Guangdong Special Support Program,the Key Laboratory of the CAS(2019DP173024)the Shenzhen Key Science and Technology Infrastructure Planning Project(ZDKJ20190204002)。
文摘Measuring eye movement is a fundamental approach in cognitive science as it provides a variety of insightful parameters that reflect brain states such as visual attention and emotions.Combining eye-tracking with multimodal neural recordings or manipulation techniques is beneficial for understanding the neural substrates of cognitive function.Many commercially-available and custom-built systems have been widely applied to awake,head-fixed small animals.However,the existing eyetracking systems used in freely-moving animals are still limited in terms of their compatibility with other devices and of the algorithm used to detect eye movements.Here,we report a novel system that integrates a general-purpose,easily compatible eye-tracking hardware with a robust eye feature-detection algorithm.With ultra-light hardware and a detachable design,the system allows for more implants to be added to the animal's exposed head and has a precise synchronization module to coordinate with other neural implants.Moreover,we systematically compared the performance of existing commonly-used pupil-detection approaches,and demonstrated that the proposed adaptive pupil feature-detection algorithm allows the analysis of more complex and dynamic eye-tracking data in freemoving animals.Synchronized eye-tracking and electroencephalogram recordings,as well as algorithm validation under five noise conditions,suggested that our system is flexibly adaptable and can be combined with a wide range of neural manipulation and recording technologies.