This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of hea...This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of heat systems.This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation.A cubature Kalman filter(CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information.Finally,a multi-timescale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for largescale systems.This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems.Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.展开更多
Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with it...Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.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
Aiming at the requirements of accurate target positioning and autonomous capability for adapting to the environmental changes of unmanned aerial vehicle(UAV),a new method for wind estimation and airspeed calibration i...Aiming at the requirements of accurate target positioning and autonomous capability for adapting to the environmental changes of unmanned aerial vehicle(UAV),a new method for wind estimation and airspeed calibration is proposed.The method is implemented to obtain both wind speed and wind direction based on the information from a GPS receiver,an air data computer and a magnetic compass,combining with the velocity vector triangle relationships among ground speed,wind speed and air speed.Considering the installation error of Pitot tube,cubature Kalman filter(CKF)is applied to determine proportionality calibration coefficient of true airspeed,thus improving the accuracy of wind field information further.The entire autonomous flight simulation is performed in a constant 2-D wind using a digital simulation platform for UAV.Simulation results show that the wind speed and wind direction can be accurately estimated both in straight line and in turning segment during the path tracking by using the proposed method.The measurement accuracies of the wind speed and wind direction are 0.62 m/s and2.57°,respectively.展开更多
Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased es...Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.展开更多
There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of track...There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter(CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown.We solve this problem by using the implicit Gauss-Helmert Sensor Model(GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM(CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.展开更多
A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and rand...A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and random measurement losses.Firstly,the Inverse-Wishart(IW)distribution is chosen to model the covariance matrix of time-varying measurement noise in the cubature Kalman filter framework.Secondly,the Bernoulli random variable is introduced as the judgement factor of the measurement losses,and the Beta distribution is selected as the conjugate prior distribution of measurement loss probability to ensure that the posterior distribution and prior distribution have the same function form.Finally,the joint posterior probability density function of the estimated variables is approximately decoupled by the variational Bayesian inference,and the fixed-point iteration approach is used to update the estimated variables.The simulation results show that the proposed VBACKF algorithm considers the comprehensive effects of system nonlinearity,time-varying measurement noise and unknown measurement loss probability,moreover,effectively improves the accuracy of target state estimation in complex scene.展开更多
利用容积卡尔曼滤波来设计粒子滤波器的重要性密度函数,并将当前的测量信息迭代到贯序重要性采样(SIS)过程中,进而提出一种基于迭代容积粒子滤波的RSSI(received signal strength indicator)蒙特卡罗定位算法.该算法使用迭代容积粒子滤...利用容积卡尔曼滤波来设计粒子滤波器的重要性密度函数,并将当前的测量信息迭代到贯序重要性采样(SIS)过程中,进而提出一种基于迭代容积粒子滤波的RSSI(received signal strength indicator)蒙特卡罗定位算法.该算法使用迭代容积粒子滤波对目标位置和无线信道衰减参数同时进行估计,采用迭代的方式对测量方程进行更新,进一步提高无线信道衰减参数的估计精度.仿真结果表明,基于迭代容积粒子滤波的RSSI蒙特卡罗定位算法对比基于无味粒子滤波的RSSI定位算法,能够有效降低室内无线定位的误差.展开更多
Modeling and state of charge （SOC） estimation of lithium-ion （Li-ion） battery are the key techniques of battery pack management system （BMS） and critical to its reliability and safety operation. An auto-regressi...Modeling and state of charge （SOC） estimation of lithium-ion （Li-ion） battery are the key techniques of battery pack management system （BMS） and critical to its reliability and safety operation. An auto-regressive with exogenous input （ARX） model is derived from RC equivalent circuit model （ECM） due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square （VFFRLS） algorithm is adopted as an adaptive parameter identifica- tion method. Based on the designed model, an SOC estimator using cubature Kalman filter （CKF） algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter （EKF） algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China(No.52060019001H)。
文摘This paper proposes a distributed real-time state estimation(RTSE)method for the combined heat and power systems(CHPSs).First,a difference-based model for the heat system is established considering the dynamics of heat systems.This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation.A cubature Kalman filter(CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information.Finally,a multi-timescale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for largescale systems.This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems.Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.
基金Supported by the National Nature Science Foundations of China(No.61300214,U1204611,61170243)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+3 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universitiesthe Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)
文摘Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.
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supported by the Pre-research Foundation of Chinese People's Liberation Army General Equipment Department(No.51325010601)
文摘Aiming at the requirements of accurate target positioning and autonomous capability for adapting to the environmental changes of unmanned aerial vehicle(UAV),a new method for wind estimation and airspeed calibration is proposed.The method is implemented to obtain both wind speed and wind direction based on the information from a GPS receiver,an air data computer and a magnetic compass,combining with the velocity vector triangle relationships among ground speed,wind speed and air speed.Considering the installation error of Pitot tube,cubature Kalman filter(CKF)is applied to determine proportionality calibration coefficient of true airspeed,thus improving the accuracy of wind field information further.The entire autonomous flight simulation is performed in a constant 2-D wind using a digital simulation platform for UAV.Simulation results show that the wind speed and wind direction can be accurately estimated both in straight line and in turning segment during the path tracking by using the proposed method.The measurement accuracies of the wind speed and wind direction are 0.62 m/s and2.57°,respectively.
基金supported by the Fundamental Research Funds for the Central Universities(xzy022020045)the National Natural Science Foundation of China(61976175)。
文摘Traditional cubature Kalman filter(CKF)is a preferable tool for the inertial navigation system(INS)/global positioning system(GPS)integration under Gaussian noises.The CKF,however,may provide a significantly biased estimate when the INS/GPS system suffers from complex non-Gaussian disturbances.To address this issue,a robust nonlinear Kalman filter referred to as cubature Kalman filter under minimum error entropy with fiducial points(MEEF-CKF)is proposed.The MEEF-CKF behaves a strong robustness against complex nonGaussian noises by operating several major steps,i.e.,regression model construction,robust state estimation and free parameters optimization.More concretely,a regression model is constructed with the consideration of residual error caused by linearizing a nonlinear function at the first step.The MEEF-CKF is then developed by solving an optimization problem based on minimum error entropy with fiducial points(MEEF)under the framework of the regression model.In the MEEF-CKF,a novel optimization approach is provided for the purpose of determining free parameters adaptively.In addition,the computational complexity and convergence analyses of the MEEF-CKF are conducted for demonstrating the calculational burden and convergence characteristic.The enhanced robustness of the MEEF-CKF is demonstrated by Monte Carlo simulations on the application of a target tracking with INS/GPS integration under complex nonGaussian noises.
基金supported in part by the National Key Research and Development Plan,China(No.2017YFB1301101)the National Natural Science Foundation of China(Nos.61673317 and 61673313)。
文摘There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking(BOT) of an aperiodic acoustic source,this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter(CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown.We solve this problem by using the implicit Gauss-Helmert Sensor Model(GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM(CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.
基金Supported by the National Natural Science Foundation of China(No.61976080)the Science and Technology Key Project of Science and TechnologyDepartment of Henan Province(No.212102310298)+1 种基金the Academic Degrees&Graduate Education Reform Project of Henan Province(No.2021SJGLX195Y)the Innovation and Quality Improvement Project for Graduate Education of Henan University(No.SYL20010101)。
文摘A novel variational Bayesian inference based on adaptive cubature Kalman filter(VBACKF)algorithm is proposed for the problem of state estimation in a target tracking system with time-varying measurement noise and random measurement losses.Firstly,the Inverse-Wishart(IW)distribution is chosen to model the covariance matrix of time-varying measurement noise in the cubature Kalman filter framework.Secondly,the Bernoulli random variable is introduced as the judgement factor of the measurement losses,and the Beta distribution is selected as the conjugate prior distribution of measurement loss probability to ensure that the posterior distribution and prior distribution have the same function form.Finally,the joint posterior probability density function of the estimated variables is approximately decoupled by the variational Bayesian inference,and the fixed-point iteration approach is used to update the estimated variables.The simulation results show that the proposed VBACKF algorithm considers the comprehensive effects of system nonlinearity,time-varying measurement noise and unknown measurement loss probability,moreover,effectively improves the accuracy of target state estimation in complex scene.
文摘利用容积卡尔曼滤波来设计粒子滤波器的重要性密度函数,并将当前的测量信息迭代到贯序重要性采样(SIS)过程中,进而提出一种基于迭代容积粒子滤波的RSSI(received signal strength indicator)蒙特卡罗定位算法.该算法使用迭代容积粒子滤波对目标位置和无线信道衰减参数同时进行估计,采用迭代的方式对测量方程进行更新,进一步提高无线信道衰减参数的估计精度.仿真结果表明,基于迭代容积粒子滤波的RSSI蒙特卡罗定位算法对比基于无味粒子滤波的RSSI定位算法,能够有效降低室内无线定位的误差.
基金supported by the National High Technology Research and Development of China 863 Program(Grant No. 2011AA11A247)
文摘Modeling and state of charge （SOC） estimation of lithium-ion （Li-ion） battery are the key techniques of battery pack management system （BMS） and critical to its reliability and safety operation. An auto-regressive with exogenous input （ARX） model is derived from RC equivalent circuit model （ECM） due to the discrete-time characteristics of BMS. For the time-varying environmental factors and the actual battery operating conditions, a variable forgetting factor recursive least square （VFFRLS） algorithm is adopted as an adaptive parameter identifica- tion method. Based on the designed model, an SOC estimator using cubature Kalman filter （CKF） algorithm is then employed to improve estimation performance and guarantee numerical stability in the computational procedure. In the battery tests, experimental results show that CKF SOC estimator has a more accuracy estimation than extended Kalman filter （EKF） algorithm, which is widely used for Li-ion battery SOC estimation, and the maximum estimation error is about 2.3%.
