This paper deals with the problem of designing robust sequential covariance intersection(SCI)fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise varian...This paper deals with the problem of designing robust sequential covariance intersection(SCI)fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances.The sensor network is partitioned into clusters by the nearest neighbor rule.Using the minimax robust estimation principle,based on the worst-case conservative sensor network system with conservative upper bounds of noise variances,and applying the unbiased linear minimum variance(ULMV)optimal estimation rule,we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources,and guarantee that the actual filtering error variances have a less-conservative upper-bound.A Lyapunov equation method for robustness analysis is proposed,by which the robustness of the local and fused Kalman filters is proved.The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved.It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter.A simulation example for a tracking system verifies the robustness and robust accuracy relations.展开更多
Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b ...Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b service is experimentally analyzed first.Then,the ionosphere-free model is established.In order to solve the problem of slow convergence for traditional PPP,an adaptive robust extend Kalman filter(AREKF)algorithm is developed.Unlike the error compensation models,it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector.The experimental results are analyzed last.Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating.Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes.Around 20%improvement in accuracy and 25%improvement in convergence speed are achieved,making it valuable for PPP processing.展开更多
State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modele...State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.展开更多
A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a...A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a binary switching sequence satisfying a conditional probability distribution,the commonest cases in engineering,such that the expectation of the measurements could be utilized during the iteration process.To consider the uncertainties in the system model,an upperbound for the estimation error covariance was obtained since its real value was unaccessible.Our filter scheme is on the basis of minimizing the obtained upper bound where we refer to the deduction of a classic Kalman filter thus calculation of the derivatives are avoided.Simulations are presented to illustrate the effectiveness of the proposed approach.展开更多
In order to solve the problem of location privacy under big data and improve the user positioning experience,a new concept of anonymous crowdsourcing-based WLAN indoor localization is proposed by employing the Micro-E...In order to solve the problem of location privacy under big data and improve the user positioning experience,a new concept of anonymous crowdsourcing-based WLAN indoor localization is proposed by employing the Micro-Electro-Mechanical System(MEMS)motion sensors as well as WLAN module in off-the-shelf smartphones.First of all,the crowdsourced motion traces with similar Received Signal Strength(RSS)sequences are assembled into a motion graph.Second,the mobility map is constructed according to traces segmentation and clustering.Third,the pixel template matching is adopted to physically label the pre-constructed mobility map.Finally,the robust Extended Kalman Filter(EKF)is designed to perform localization by matching the newly-collected RSS measurements against the mobility map.The extensive experimental results show that the proposed approach is capable of constructing a physically-labeled mobility map from the sporadically-collected crowdsourced motion traces as well as achieving satisfactory localization accuracy in a cost-efficient manner.展开更多
In order to meet the requirements of high-precision vehicle positioning in complex scenes,an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero ...In order to meet the requirements of high-precision vehicle positioning in complex scenes,an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed,non-integrity,attitude,and odometer constraint models.In this model,the robust equivalent gain matrix is constructed by the IGG-Ⅲmethod to weaken the influence of gross error,and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment,so as to improve the solution performance of filtering system and realize high-precision position,attitude and velocity measurement when GNSS signal is unlocked.A real test on a road over 600 km demonstrates that,in about 100 km shaded environment,the fixed rate of GNSS ambiguity resolution in the shaded road is 10%higher than that of GNSS only ambiguity resolution.For all the test,the positioning accuracy can reach the centimeter level in an open environment,better than 0.6 m in the tree shaded environment,better than 1.5 m in the three-dimensional traffic environment,and can still maintain a positioning accuracy of 0.1 m within 10 s when the satellite is unlocked in the tunnel scene.The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints,which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.展开更多
Sideslip angle,yaw rate,and vehicle velocity are essential for intelligent vehicle control.Since these vehicle states are not measured directly,some Kalman-based approaches have been developed to estimate these states...Sideslip angle,yaw rate,and vehicle velocity are essential for intelligent vehicle control.Since these vehicle states are not measured directly,some Kalman-based approaches have been developed to estimate these states using in-vehicle sensors.However,the existing studies seldom account for the influence of sensor data loss on estimation accuracy.In addition,the process and measurement noise change during the estimation process because of the various driving conditions.To address these problems,an expectation-maximization robust extended Kalman filter(EMREKF)is proposed.Firstly,a robust extended Kalman filter(REKF)is developed to deal with the impact of missing measurements.Then,an improved expectation maximization(EM)algorithm that considers data loss is presented to update the noise parameter of the REKF dynamically.Finally,the improved EM is fused with the REKF to form the EMREKF to estimate vehicle state.The experimental results demonstrate that the EMREKF outperforms EKF,REKF,and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.展开更多
A control strategy based on LQG/LTR theory for steer-by-wire (SBW) system is proposed in this paper. Firstly, the models of the SBW system and the whole vehicle are constructed. econdly, the control strategy of LQG f...A control strategy based on LQG/LTR theory for steer-by-wire (SBW) system is proposed in this paper. Firstly, the models of the SBW system and the whole vehicle are constructed. econdly, the control strategy of LQG for SBW system is proposed, in which the LTR is utilized to eliminate the effect from the Kalman filter. Thirdly, simulations based on the co- simulation platform of MATLAB/Simulink and Carsim are performed with the proposed control strategy to identify its performance. At last, field experiments are conducted to further verify the feasibility of the proposed control strategy in real application. The simulation and experiment results indicate that the proposed control strategy has good stability, robustness and feasibility in real application, and is more effective in practical application of SBW system.展开更多
基金Supported by National Natural Science Foundation of China(60874063)Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province(YJSCX2012-263HLJ)
文摘This paper deals with the problem of designing robust sequential covariance intersection(SCI)fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances.The sensor network is partitioned into clusters by the nearest neighbor rule.Using the minimax robust estimation principle,based on the worst-case conservative sensor network system with conservative upper bounds of noise variances,and applying the unbiased linear minimum variance(ULMV)optimal estimation rule,we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources,and guarantee that the actual filtering error variances have a less-conservative upper-bound.A Lyapunov equation method for robustness analysis is proposed,by which the robustness of the local and fused Kalman filters is proved.The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved.It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter.A simulation example for a tracking system verifies the robustness and robust accuracy relations.
文摘Beidou-3 navigation satellite system(BDS-3)initiated a real-time service for precise point positioning(PPP)using the B2b signal,mainly for users in China and surrounding areas.In this paper,the performance of PPP-B2b service is experimentally analyzed first.Then,the ionosphere-free model is established.In order to solve the problem of slow convergence for traditional PPP,an adaptive robust extend Kalman filter(AREKF)algorithm is developed.Unlike the error compensation models,it reflects the noise information in real time by adjusting the covariance matrix of the measurements and the weight matrix of the state vector.The experimental results are analyzed last.Evaluation results indicate that the corrections provided by PPP-B2b can significantly reduce the discontinuous error of the orbits and clock offsets caused by broadcast ephemeris updating.Positioning results confirm that AREKF outperforms EKF both in static and kinematic modes.Around 20%improvement in accuracy and 25%improvement in convergence speed are achieved,making it valuable for PPP processing.
基金Supported by the National Natural Science Foundation of China （20476007, 20676013）.
文摘State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.
基金Supported by the National Natural Science Foundation for Outstanding Youth(61422102)
文摘A robust finite-horizon Kalman filter is designed for linear discrete-time systems subject to norm-bounded uncertainties in the modeling parameters and missing measurements.The missing measurements were described by a binary switching sequence satisfying a conditional probability distribution,the commonest cases in engineering,such that the expectation of the measurements could be utilized during the iteration process.To consider the uncertainties in the system model,an upperbound for the estimation error covariance was obtained since its real value was unaccessible.Our filter scheme is on the basis of minimizing the obtained upper bound where we refer to the deduction of a classic Kalman filter thus calculation of the derivatives are avoided.Simulations are presented to illustrate the effectiveness of the proposed approach.
基金the National Natural Science Foundation of China(61771083,61704015)Program for Changjiang Scholars and Innovative Research Team in University(IRT1299)+2 种基金Special Fund of Chongqing Key Laboratory(CSTC),Fundamental and Frontier Research Project of Chongqing(cstc2017jcyjAX0380,cstc2015jcyjBX0065)University Outstanding Achievement Transformation Project of Chongqing(KJZH17117)Postgraduate Scientific Research and Innovation Project of Chongqing(CYS17221).
文摘In order to solve the problem of location privacy under big data and improve the user positioning experience,a new concept of anonymous crowdsourcing-based WLAN indoor localization is proposed by employing the Micro-Electro-Mechanical System(MEMS)motion sensors as well as WLAN module in off-the-shelf smartphones.First of all,the crowdsourced motion traces with similar Received Signal Strength(RSS)sequences are assembled into a motion graph.Second,the mobility map is constructed according to traces segmentation and clustering.Third,the pixel template matching is adopted to physically label the pre-constructed mobility map.Finally,the robust Extended Kalman Filter(EKF)is designed to perform localization by matching the newly-collected RSS measurements against the mobility map.The extensive experimental results show that the proposed approach is capable of constructing a physically-labeled mobility map from the sporadically-collected crowdsourced motion traces as well as achieving satisfactory localization accuracy in a cost-efficient manner.
基金Youth Program of National Natural Science Foundation of China (No. 41904029)Scientific Research Project of Beijing Educational Committee (No. KM202010016009)。
文摘In order to meet the requirements of high-precision vehicle positioning in complex scenes,an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed,non-integrity,attitude,and odometer constraint models.In this model,the robust equivalent gain matrix is constructed by the IGG-Ⅲmethod to weaken the influence of gross error,and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment,so as to improve the solution performance of filtering system and realize high-precision position,attitude and velocity measurement when GNSS signal is unlocked.A real test on a road over 600 km demonstrates that,in about 100 km shaded environment,the fixed rate of GNSS ambiguity resolution in the shaded road is 10%higher than that of GNSS only ambiguity resolution.For all the test,the positioning accuracy can reach the centimeter level in an open environment,better than 0.6 m in the tree shaded environment,better than 1.5 m in the three-dimensional traffic environment,and can still maintain a positioning accuracy of 0.1 m within 10 s when the satellite is unlocked in the tunnel scene.The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints,which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.
基金supported by the National Natural Science Foundation of China(Grant No.52402482).
文摘Sideslip angle,yaw rate,and vehicle velocity are essential for intelligent vehicle control.Since these vehicle states are not measured directly,some Kalman-based approaches have been developed to estimate these states using in-vehicle sensors.However,the existing studies seldom account for the influence of sensor data loss on estimation accuracy.In addition,the process and measurement noise change during the estimation process because of the various driving conditions.To address these problems,an expectation-maximization robust extended Kalman filter(EMREKF)is proposed.Firstly,a robust extended Kalman filter(REKF)is developed to deal with the impact of missing measurements.Then,an improved expectation maximization(EM)algorithm that considers data loss is presented to update the noise parameter of the REKF dynamically.Finally,the improved EM is fused with the REKF to form the EMREKF to estimate vehicle state.The experimental results demonstrate that the EMREKF outperforms EKF,REKF,and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.
基金supported by the National Natural Science Foundation of China (Grant No. 51375007)the Fundamental Research Funds for the Central Universities (Grant No. NE2016002)Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems (Grant No.GZKF-201605)
文摘A control strategy based on LQG/LTR theory for steer-by-wire (SBW) system is proposed in this paper. Firstly, the models of the SBW system and the whole vehicle are constructed. econdly, the control strategy of LQG for SBW system is proposed, in which the LTR is utilized to eliminate the effect from the Kalman filter. Thirdly, simulations based on the co- simulation platform of MATLAB/Simulink and Carsim are performed with the proposed control strategy to identify its performance. At last, field experiments are conducted to further verify the feasibility of the proposed control strategy in real application. The simulation and experiment results indicate that the proposed control strategy has good stability, robustness and feasibility in real application, and is more effective in practical application of SBW system.
