Shaking table tests are widely used to evaluate seismic effects on railway structures,but accurately measuring rail displacement remains a significant challenge owing to the nonlinear characteristics of large displace...Shaking table tests are widely used to evaluate seismic effects on railway structures,but accurately measuring rail displacement remains a significant challenge owing to the nonlinear characteristics of large displacements,ambient noise interference,and limitations in displacement meter installation.In this paper,a novel method that integrates the Kanade-Lucas-Tomasi(KLT)feature tracker with an extended Kalman filter(EKF)is presented for measuring rail displacement during shaking table tests.The method employs KLT feature tracker and a random sample consensus algorithm to extract and track key feature points,while EKF optimally estimates dynamic states by accounting for system noise and observation errors.Shaking table test results demonstrate that the proposed method achieves an acceleration root mean square error of 0.300 m/s^(2)and a correlation with accelerometer data exceeding 99.7%,significantly outper-forming the original KLT approach.This innovative method provides a more efficient and reliable solution for measuring rail displacement under large nonlinear vibrations.展开更多
To overcome the shortcomings of traditional artificial spraying pesticides and make more efficient prevention of diseases and pests,a coaxial sixteen-rotor unmanned aerial vehicle(UAV)with pesticide spraying system is...To overcome the shortcomings of traditional artificial spraying pesticides and make more efficient prevention of diseases and pests,a coaxial sixteen-rotor unmanned aerial vehicle(UAV)with pesticide spraying system is designed.The coaxial sixteen-rotor UAV’s basic structure and attitude estimation method are explained.The whole system weights 25 kg,cruising speed can reach 15 m/s,and the flight time is more than 20 min.When the UAV takes large load,the traditional extended Kalman filter(EKF)attitude estimation method can not meet the work requirements under the condition of strong vibration,the attitude measure accuracy is poor and the attitude angle divergence is easily caused.Hence an attitude estimation method based on EKF algorithm with 22 dimensional state vector is proposed which can solve these problems.The UAV system consists of STM32F429 as controller,integrating following measure sensors:accelerometer and gyroscope MPU6000,magnetometer LSM303D,GPS NEO-M8N and barometer.The attitude unit quaternion,velocity,position,earth magnetic field,biases error of gyroscope,accelerometer and magnetometer are introduced as the inertial navigation systems(INS)state vector,while magnetometer,global positioning system(GPS)and barometer are introduced as observation vector,thus making the estimate of the navigation information more accurate.The control strategy of coaxial sixteen-rotor UAV is based on the control method of combining active disturbance rejection control(ADRC)and proportion integral derivative(PID)control.Actual flight data are used to verify the algorithm,and the static experiment shows that the precision of roll angle and pitch angle of the algorithm are±0.1°,the precision of yaw angle is±0.2°.The attitude angle output of MTi sensor is used as reference.The dynamic experiment shows that the accuracy of attitude estimated by EKF algorithm is quite similar to that of MTi’s output,moreover,the algorithm has good real-time performance which meets the need of high maneuverability of agricultural UAV.展开更多
A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filte...A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.展开更多
Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial v...Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial velocity obtained in the signal processing are introduced into the measurement vector by coordinate transformation.In order to solve the problem of high nonlinearity of the radial acceleration,radial velocity and the state vector,a new algorithm of multi-parameter sequential extended Kalman filter( MSEKF) is proposed.The tracking performance of this algorithm is tested and compared with the other tracking algorithms.It is shown that the proposed algorithm outperforms these algorithms in strong and weak maneuvering environments.展开更多
In the case of a medium-long baseline, for real-time kinematic (RTK) positioning, the fixed rate of integer ambiguity is low due to the distance between the base station and the observation station. Moreover, the atmo...In the case of a medium-long baseline, for real-time kinematic (RTK) positioning, the fixed rate of integer ambiguity is low due to the distance between the base station and the observation station. Moreover, the atmospheric delay after differential processing cannot be ignored. For correcting the residual atmospheric errors, we proposed a GPS/BDS/Galileo/GLONASS four-system fusion RTK positioning algorithm, which is based on the extended Kalman filter (EKF) algorithm. After realizing the spatio-temporal unification of multiple global navigation satellite systems (GNSSs), we introduced a parameter estimation of atmospheric errors based on the EKF model, using the least-squares integer ambiguity decorrelation adjustment (LAMBDA) to calculate the integer ambiguity. After conducting experiments for different baselines, the proposed RTK positioning algorithm can achieve centimeter-level positioning accuracy in the case of medium-long baselines. In addition, the time required to solve the fixed solution is shorter than that of the traditional RTK positioning algorithm.展开更多
A new method of unscented extended Kalman filter （UEKF） for nonlinear system is presented. This new method is a combination of the unscented transformation and the extended Kalman filter （EKF）. The extended Kalman...A new method of unscented extended Kalman filter （UEKF） for nonlinear system is presented. This new method is a combination of the unscented transformation and the extended Kalman filter （EKF）. The extended Kalman filter is similar to that in a conventional EKF. However, in every running step of the EKF the unscented transformation is running, the deterministic sample is caught by unscented transformation, then posterior mean of non- lineadty is caught by propagating, but the posterior covariance of nonlinearity is caught by linearizing. The accuracy of new method is a little better than that of the unscented Kalman filter （UKF）, however, the computational time of the UEKF is much less than that of the UKF.展开更多
A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes a...A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.展开更多
An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and pot...An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.展开更多
Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-p...Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-posed.The deviation in the estimation cannot be avoided.Furthermore,the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions.To solve the above problems in GPS dynamic positioning by using EKF,a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American.The method separates the spatial parts from temporal parts during processing the GPS filter problems,and solves the nonlinear GPS dynamic positioning,thus getting stable and reliable dynamic positioning solutions.展开更多
In the strapdown inertial navigation system,the attitude information is obtained through an inertial measurement unit(IMU)device,which mainly includes a triaxial gyroscope,a triaxial accelerometer and a triaxial magne...In the strapdown inertial navigation system,the attitude information is obtained through an inertial measurement unit(IMU)device,which mainly includes a triaxial gyroscope,a triaxial accelerometer and a triaxial magnetometer.However,IMU sensors have system noise and drift errors,and these errors can accumulate over time,which makes it difficult to control the attitude accuracy.In order to solve the problems of gyro drift over time and random errors generated by the surrounding environment,this paper presents an attitude calculation algorithm based on wavelet neural network-extended Kalman filter(WNN-EKF).The wavelet neural network(WNN)is used to optimize the model and compensate the extended Kalman filter’s own model error.Through the semi-physical simulation experiment,the results show that the algorithm improves the accuracy of attitude calculation and enhances the self-adaptability to the environment.展开更多
To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-bas...To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-based nonlinear filtering methods,we design a new method for estimating noise statistical characteristics of nonlinear systems based on the credibility Kalman Filter(KF)theory considering noise correlation.This method first extends credibility to the Unscented Kalman Filter(UKF)and Extended Kalman Filter(EKF)based on the credibility theory.Further,an optimization model for nonlinear credibility under noise related conditions is established considering noise correlation.A combination of filtering smoothing and credibility iteration formula is used to improve the real-time performance of the nonlinear adaptive credibility KF algorithm,further expanding its application scenarios,and the derivation process of the formula theory is provided.Finally,the performance of the nonlinear credibility filtering algorithm is simulated and analyzed from multiple perspectives,and a comparative analysis conducted on specific experimental data.The simulation and experimental results show that the proposed credibility EKF and credibility UKF algorithms can estimate the noise covariance more accurately and effectively with lower average estimation time than traditional methods,indicating that the proposed algorithm has stable estimation performance and good real-time performance.展开更多
When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the feature...When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.展开更多
Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC es...Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.展开更多
基金The National Key Research and Development Program of China(No.2021YFB2600600,2021YFB2600601)the National Natural Science Foundation of China(No.52408456)+2 种基金China Postdoctoral Science Foundation(No.2022M720533)College Students’Innovative Entrepreneurial Training Plan Program(No.202410710009)Key Research and Development Program of Shaanxi,China(No.2024SF-YBXM-659).
文摘Shaking table tests are widely used to evaluate seismic effects on railway structures,but accurately measuring rail displacement remains a significant challenge owing to the nonlinear characteristics of large displacements,ambient noise interference,and limitations in displacement meter installation.In this paper,a novel method that integrates the Kanade-Lucas-Tomasi(KLT)feature tracker with an extended Kalman filter(EKF)is presented for measuring rail displacement during shaking table tests.The method employs KLT feature tracker and a random sample consensus algorithm to extract and track key feature points,while EKF optimally estimates dynamic states by accounting for system noise and observation errors.Shaking table test results demonstrate that the proposed method achieves an acceleration root mean square error of 0.300 m/s^(2)and a correlation with accelerometer data exceeding 99.7%,significantly outper-forming the original KLT approach.This innovative method provides a more efficient and reliable solution for measuring rail displacement under large nonlinear vibrations.
基金the National Natural Science Foundation of China(No.11372309,61304017)Youth Innovation Promotion Association(No.2014192)+1 种基金the Provincial Special Funds Project of Science and Technology Cooperation(No.2017SYHZ0024)Key Technology Development Project of Jilin Province(No.20150204074GX).
文摘To overcome the shortcomings of traditional artificial spraying pesticides and make more efficient prevention of diseases and pests,a coaxial sixteen-rotor unmanned aerial vehicle(UAV)with pesticide spraying system is designed.The coaxial sixteen-rotor UAV’s basic structure and attitude estimation method are explained.The whole system weights 25 kg,cruising speed can reach 15 m/s,and the flight time is more than 20 min.When the UAV takes large load,the traditional extended Kalman filter(EKF)attitude estimation method can not meet the work requirements under the condition of strong vibration,the attitude measure accuracy is poor and the attitude angle divergence is easily caused.Hence an attitude estimation method based on EKF algorithm with 22 dimensional state vector is proposed which can solve these problems.The UAV system consists of STM32F429 as controller,integrating following measure sensors:accelerometer and gyroscope MPU6000,magnetometer LSM303D,GPS NEO-M8N and barometer.The attitude unit quaternion,velocity,position,earth magnetic field,biases error of gyroscope,accelerometer and magnetometer are introduced as the inertial navigation systems(INS)state vector,while magnetometer,global positioning system(GPS)and barometer are introduced as observation vector,thus making the estimate of the navigation information more accurate.The control strategy of coaxial sixteen-rotor UAV is based on the control method of combining active disturbance rejection control(ADRC)and proportion integral derivative(PID)control.Actual flight data are used to verify the algorithm,and the static experiment shows that the precision of roll angle and pitch angle of the algorithm are±0.1°,the precision of yaw angle is±0.2°.The attitude angle output of MTi sensor is used as reference.The dynamic experiment shows that the accuracy of attitude estimated by EKF algorithm is quite similar to that of MTi’s output,moreover,the algorithm has good real-time performance which meets the need of high maneuverability of agricultural UAV.
基金supported by National Natural Science Foundation of China (Nos.62265010,62061024)Gansu Province Science and Technology Plan (No.23YFGA0062)Gansu Province Innovation Fund (No.2022A-215)。
文摘A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target tracking.First,the steps of extended Kalman filtering(EKF)are introduced.Second,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile target.Finally,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed algorithm.The comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.
基金National Natural Science Foundations of China(Nos.61531020,61471383)
文摘Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial velocity obtained in the signal processing are introduced into the measurement vector by coordinate transformation.In order to solve the problem of high nonlinearity of the radial acceleration,radial velocity and the state vector,a new algorithm of multi-parameter sequential extended Kalman filter( MSEKF) is proposed.The tracking performance of this algorithm is tested and compared with the other tracking algorithms.It is shown that the proposed algorithm outperforms these algorithms in strong and weak maneuvering environments.
文摘In the case of a medium-long baseline, for real-time kinematic (RTK) positioning, the fixed rate of integer ambiguity is low due to the distance between the base station and the observation station. Moreover, the atmospheric delay after differential processing cannot be ignored. For correcting the residual atmospheric errors, we proposed a GPS/BDS/Galileo/GLONASS four-system fusion RTK positioning algorithm, which is based on the extended Kalman filter (EKF) algorithm. After realizing the spatio-temporal unification of multiple global navigation satellite systems (GNSSs), we introduced a parameter estimation of atmospheric errors based on the EKF model, using the least-squares integer ambiguity decorrelation adjustment (LAMBDA) to calculate the integer ambiguity. After conducting experiments for different baselines, the proposed RTK positioning algorithm can achieve centimeter-level positioning accuracy in the case of medium-long baselines. In addition, the time required to solve the fixed solution is shorter than that of the traditional RTK positioning algorithm.
文摘A new method of unscented extended Kalman filter （UEKF） for nonlinear system is presented. This new method is a combination of the unscented transformation and the extended Kalman filter （EKF）. The extended Kalman filter is similar to that in a conventional EKF. However, in every running step of the EKF the unscented transformation is running, the deterministic sample is caught by unscented transformation, then posterior mean of non- lineadty is caught by propagating, but the posterior covariance of nonlinearity is caught by linearizing. The accuracy of new method is a little better than that of the unscented Kalman filter （UKF）, however, the computational time of the UEKF is much less than that of the UKF.
基金Project supported by the National Natural Science Foundation of China （Grant No.60572157）, and the National High- Technology Research and Development Program of China （Grant No.2003AA123310）
文摘A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.
文摘An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.
文摘Because of the ignored items after linearization,the extended Kalman filter(EKF)becomes a form of suboptimal gradient descent algorithm.The emanative tendency exists in GPS solution when the filter equations are ill-posed.The deviation in the estimation cannot be avoided.Furthermore,the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions.To solve the above problems in GPS dynamic positioning by using EKF,a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American.The method separates the spatial parts from temporal parts during processing the GPS filter problems,and solves the nonlinear GPS dynamic positioning,thus getting stable and reliable dynamic positioning solutions.
基金National Natural Science Foundation of China(No.61863024)Basic Research Innovation Group Program of Gansu Province(No.1606RJIA327)+2 种基金Higher Education Research Project Funding of Gansu Province(No.2018C-11)Natural Foundation of Gansu Province(No.18JR3RA107)Science and Technology Program Funding of Gansu Province(No.18CX3ZA004)。
文摘In the strapdown inertial navigation system,the attitude information is obtained through an inertial measurement unit(IMU)device,which mainly includes a triaxial gyroscope,a triaxial accelerometer and a triaxial magnetometer.However,IMU sensors have system noise and drift errors,and these errors can accumulate over time,which makes it difficult to control the attitude accuracy.In order to solve the problems of gyro drift over time and random errors generated by the surrounding environment,this paper presents an attitude calculation algorithm based on wavelet neural network-extended Kalman filter(WNN-EKF).The wavelet neural network(WNN)is used to optimize the model and compensate the extended Kalman filter’s own model error.Through the semi-physical simulation experiment,the results show that the algorithm improves the accuracy of attitude calculation and enhances the self-adaptability to the environment.
基金supported by the National Natural Science Foundation of China(No.62033010)the Qing Lan Project of Jiangsu Province,China(No.R2023Q07)the Aeronautical Science Foundation of China(No.2019460T5001).
文摘To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-based nonlinear filtering methods,we design a new method for estimating noise statistical characteristics of nonlinear systems based on the credibility Kalman Filter(KF)theory considering noise correlation.This method first extends credibility to the Unscented Kalman Filter(UKF)and Extended Kalman Filter(EKF)based on the credibility theory.Further,an optimization model for nonlinear credibility under noise related conditions is established considering noise correlation.A combination of filtering smoothing and credibility iteration formula is used to improve the real-time performance of the nonlinear adaptive credibility KF algorithm,further expanding its application scenarios,and the derivation process of the formula theory is provided.Finally,the performance of the nonlinear credibility filtering algorithm is simulated and analyzed from multiple perspectives,and a comparative analysis conducted on specific experimental data.The simulation and experimental results show that the proposed credibility EKF and credibility UKF algorithms can estimate the noise covariance more accurately and effectively with lower average estimation time than traditional methods,indicating that the proposed algorithm has stable estimation performance and good real-time performance.
基金National Natural Science Foundation of China(Nos.61662070,61363059)Youth Science Fund Project of Lanzhou Jiaotong University(No.2018036)。
文摘When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.
文摘Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.
基金This work was supported by the National Natural Science Foundation of China （51507015, 61773402, 61540037, 71271215, 61233008, 51425701, 70921001, 51577014）, the Natural Science Foundation of Hunan Province （2015JJ3008）, the Key Laboratory of Renewable Energy Electric-Technology of Hunan Province （2014ZNDL002）, and Hunan Province Science and Technology Program（2015NK3035）.
