In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress h...In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress high-frequency noise,an infinite impulse response filter(IIRF)is introduced at the front end of ASTUKF to preprocess the original data.Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering.After that,the data from the experiment were tested on the hardware experiment platform.The experimental results show that compared to the traditional extended Kalman filter(EKF)and unscented Kalman filter(UKF)algorithms,the root mean square error(RMSE)of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5%and 36.1%;the RMSE of the pitch axis results decreases by nearly 58.4%and 51.5%,respectively;and the RMSE of the yaw axis results decreases almost 62.8%and 50.9%,correspondingly.The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.展开更多
Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection metho...Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.展开更多
When the proton exchange membrane fuel cell(PEMFC)system is running,there will be a condition that does not require power output for a short time.In order to achieve zero power output under low power consumption,it is...When the proton exchange membrane fuel cell(PEMFC)system is running,there will be a condition that does not require power output for a short time.In order to achieve zero power output under low power consumption,it is necessary to consider the diversity of control targets and the complexity of dynamic models,which brings the challenge of high-precision tracking control of the stack output power and cathode intake flow.For system idle speed control,a modelbased nonlinear control framework is constructed in this paper.Firstly,the nonlinear dynamic model of output power and cathode intake flow is derived.Secondly,a control scheme combining nonlinear extended Kalman filter observer and state feedback controller is designed.Finally,the control scheme is verified on the PEMFC experimental platform and compared with the proportion-integration-differentiation(PID)controller.The experimental results show that the control strategy proposed in this paper can realize the idle speed control of the fuel cell system and achieve the purpose of zero power output.Compared with PID controller,it has faster response speed and better system dynamics.展开更多
In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GP...In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GPS real-time deformation series with a high sampling rate contain coloured noise, the multiple Kalman filter model requires the white noise, and the multiple Kalman filters model is augmented by a shaping filter in order to reduce the colored noise; secondly, the multiple Kalman filters model with shaping filter can detect the deformation epoch in real-time and improve the quality of GPS measurements for the real-time deformation applications. Based on the comparisons of the applications in different GPS time series with different models, the advantages of the proposed model were illustrated. The proposed model can reduce the colored noise, detect the smaller changes, and improve the precision of the detected deformation epoch.展开更多
This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation ...This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.展开更多
The structural dynamic response reconstruction technology can extract unmeasured information from limited measured data,significantly impacting vibration control,load identification,parameter identification,fault diag...The structural dynamic response reconstruction technology can extract unmeasured information from limited measured data,significantly impacting vibration control,load identification,parameter identification,fault diagnosis,and related fields.This paper proposes a dynamic response reconstruction method based on the Kalman filter,which simultaneously identifies external excitation and reconstructs dynamic responses at unmeasured positions.The weighted least squares method determines the load weighting matrix for excitation identification,while the minimum variance unbiased estimation determines the Kalman filter gain.The excitation prediction Kalman filter is constructed through time,excitation,and measurement updates.Subsequently,the response at the target point is reconstructed using the state vector,observation matrix,and excitation influence matrix obtained through the excitation prediction Kalman filter algorithm.An algorithm for reconstructing responses in continuous system using the excitation prediction Kalman filtering algorithm in modal space is derived.The proposed structural dynamic response reconstruction method evaluates the response reconstruction and the load identification performance under various load types and errors through simulation examples.Results demonstrate the accurate excitation identification under different load conditions and simultaneous reconstruction of target point responses,verifying the feasibility and reliability of the proposed method.展开更多
In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on opti...In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.展开更多
Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when ...Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.展开更多
The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with ...The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.展开更多
The tire-road friction coefficient(TRFC)plays a critical role in vehicle safety and dynamic stability,with model-based approaches being the primary method for TRFC estimation.However,the accuracy of these methods is o...The tire-road friction coefficient(TRFC)plays a critical role in vehicle safety and dynamic stability,with model-based approaches being the primary method for TRFC estimation.However,the accuracy of these methods is often constrained by the complexity of tire force expressions and uncertainties in tire model parameters,particularly under diverse and complex driving conditions.To address these challenges,this paper proposes a novel data enforced unscented Kalman filter(DeUKF)approach for precise TRFC estimation in intelligent chassis systems.First,an Unscented Kalman Filter is constructed using a nominal tire model-based vehicle dynamics formulation.Then,leveraging Willems’Fundamental Lemma and historical real-world driving data,the vehicle dynamics model is adap-tively corrected within the Unscented Kalman Filter framework.This correction effectively mitigates the adverse effects of tire model uncertainties,thereby enhancing TRFC estimation accuracy.Finally,real vehicle experiments are conducted to validate the effectiveness and superiority of the proposed method.展开更多
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
The extended Kalman filter(EKF)is extensively applied in integrated navigation systems that combine the global navigation satellite system(GNSS)and strap-down inertial navigation system(SINS).However,the performance o...The extended Kalman filter(EKF)is extensively applied in integrated navigation systems that combine the global navigation satellite system(GNSS)and strap-down inertial navigation system(SINS).However,the performance of the EKF can be severely impacted by non-Gaussian noise and measurement noise uncertainties,making it difficult to achieve optimal GNSS/INS integration.Dealing with non-Gaussian noise remains a significant challenge in filter development today.Therefore,the maximum correntropy criterion(MCC)is utilized in EKFs to manage heavytailed measurement noise.However,its capability to handle non-Gaussian process noise and unknown disturbances remains largely unexplored.In this paper,we extend correntropy from using a single kernel to a multi-kernel approach.This leads to the development of a multi-kernel maximum correntropy extended Kalman filter(MKMC-EKF),which is designed to effectively manage multivariate non-Gaussian noise and disturbances.Further,theoretical analysis,including advanced stability proofs,can enhance understanding,while hybrid approaches integrating MKMC-EKF with particle filters may improve performance in nonlinear systems.The MKMC-EKF enhances estimation accuracy using a multi-kernel bandwidth approach.As bandwidth increases,the filter’s sensitivity to non-Gaussian features decreases,and its behavior progressively approximates that of the iterated EKF.The proposed approach for enhancing positioning in navigation is validated through performance evaluations,which demonstrate its practical applications in real-world systems like GPS navigation and measuring radar targets.展开更多
This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable...This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.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
The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysi...The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.展开更多
Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers...Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.展开更多
Using a gravity anomaly covariance function based on the second-order Ganssian Markov gravity anomaly potential model, the state equation of a gravity anomaly signal is obtained in marine gravimetry. Combined with the...Using a gravity anomaly covariance function based on the second-order Ganssian Markov gravity anomaly potential model, the state equation of a gravity anomaly signal is obtained in marine gravimetry. Combined with the system state equation and the measurement equation, a new method of the cascade Kalman filter is proposed and applied to the correction of gravity anomaly distortion. In the signal processing procedure, an inverse Kalman filter is used to restore the gravity anomaly signal and high frequency noises first. Then an adaptive Kalman filter, which uses the gravity anomaly state equation as the system equation, is set to estimate the actual gravity anomaly data. Emulations and experiments indicate that both the cascade Kalman filter method and the single inverse Kalman filter method are effective in alleviating the distortion of the gravity anomaly signal, but the performance of the cascade Kalman filter method is better than that of the single inverse Kalman filter method.展开更多
To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environme...To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.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
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
基金supported by the Key Research and Development Program of Shaanxi Province(No.2024NC-YBXM-246)the Shaanxi Provincial Science and Technology Department(No.2024JC-YBQN-0725)+1 种基金the Education Department of Shaanxi Province(No.23JK0371)the Shaanxi University of Technology(No.SLGRCQD2318).
文摘In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress high-frequency noise,an infinite impulse response filter(IIRF)is introduced at the front end of ASTUKF to preprocess the original data.Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering.After that,the data from the experiment were tested on the hardware experiment platform.The experimental results show that compared to the traditional extended Kalman filter(EKF)and unscented Kalman filter(UKF)algorithms,the root mean square error(RMSE)of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5%and 36.1%;the RMSE of the pitch axis results decreases by nearly 58.4%and 51.5%,respectively;and the RMSE of the yaw axis results decreases almost 62.8%and 50.9%,correspondingly.The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.
基金Supported by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(22KJB520012)the Research Project on Higher Education Reform in Jiangsu Province(2023JSJG781)the College Student Innovation and Entrepreneurship Training Program Project(202313571008Z)。
文摘Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.
基金Supported by the Major Science and Technology Projects in Jilin Province and Changchun City(20220301010GX).
文摘When the proton exchange membrane fuel cell(PEMFC)system is running,there will be a condition that does not require power output for a short time.In order to achieve zero power output under low power consumption,it is necessary to consider the diversity of control targets and the complexity of dynamic models,which brings the challenge of high-precision tracking control of the stack output power and cathode intake flow.For system idle speed control,a modelbased nonlinear control framework is constructed in this paper.Firstly,the nonlinear dynamic model of output power and cathode intake flow is derived.Secondly,a control scheme combining nonlinear extended Kalman filter observer and state feedback controller is designed.Finally,the control scheme is verified on the PEMFC experimental platform and compared with the proportion-integration-differentiation(PID)controller.The experimental results show that the control strategy proposed in this paper can realize the idle speed control of the fuel cell system and achieve the purpose of zero power output.Compared with PID controller,it has faster response speed and better system dynamics.
基金Project(20120022120011)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of ChinaProject(2652012062)supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GPS real-time deformation series with a high sampling rate contain coloured noise, the multiple Kalman filter model requires the white noise, and the multiple Kalman filters model is augmented by a shaping filter in order to reduce the colored noise; secondly, the multiple Kalman filters model with shaping filter can detect the deformation epoch in real-time and improve the quality of GPS measurements for the real-time deformation applications. Based on the comparisons of the applications in different GPS time series with different models, the advantages of the proposed model were illustrated. The proposed model can reduce the colored noise, detect the smaller changes, and improve the precision of the detected deformation epoch.
基金National Natural Science Foundation of China(Grant No.62266045)National Science and Technology Major Project of China(No.2022YFE0138600)。
文摘This paper presents a manifold-optimized Error-State Kalman Filter(ESKF)framework for unmanned aerial vehicle(UAV)pose estimation,integrating Inertial Measurement Unit(IMU)data with GPS or LiDAR to enhance estimation accuracy and robustness.We employ a manifold-based optimization approach,leveraging exponential and logarithmic mappings to transform rotation vectors into rotation matrices.The proposed ESKF framework ensures state variables remain near the origin,effectively mitigating singularity issues and enhancing numerical stability.Additionally,due to the small magnitude of state variables,second-order terms can be neglected,simplifying Jacobian matrix computation and improving computational efficiency.Furthermore,we introduce a novel Kalman filter gain computation strategy that dynamically adapts to low-dimensional and high-dimensional observation equations,enabling efficient processing across different sensor modalities.Specifically,for resource-constrained UAV platforms,this method significantly reduces computational cost,making it highly suitable for real-time UAV applications.
基金supported by the National Natural Science Foundation of China(Nos.12372066,U23B6009,52171261)the Aeronautical Science Fund(No.20240013052002)the Qing Lan Project。
文摘The structural dynamic response reconstruction technology can extract unmeasured information from limited measured data,significantly impacting vibration control,load identification,parameter identification,fault diagnosis,and related fields.This paper proposes a dynamic response reconstruction method based on the Kalman filter,which simultaneously identifies external excitation and reconstructs dynamic responses at unmeasured positions.The weighted least squares method determines the load weighting matrix for excitation identification,while the minimum variance unbiased estimation determines the Kalman filter gain.The excitation prediction Kalman filter is constructed through time,excitation,and measurement updates.Subsequently,the response at the target point is reconstructed using the state vector,observation matrix,and excitation influence matrix obtained through the excitation prediction Kalman filter algorithm.An algorithm for reconstructing responses in continuous system using the excitation prediction Kalman filtering algorithm in modal space is derived.The proposed structural dynamic response reconstruction method evaluates the response reconstruction and the load identification performance under various load types and errors through simulation examples.Results demonstrate the accurate excitation identification under different load conditions and simultaneous reconstruction of target point responses,verifying the feasibility and reliability of the proposed method.
基金supported by Natural Science Foundation of Gansu Province(No.20JR5RA407).
文摘In order to reduce the error judgment of outliers in vehicle temperature prediction and improve the accuracy of single-station processor prediction data,a Kalman filter multi-information fusion algorithm based on optimized P-Huber weight function was proposed.The algorithm took Kalman filter(KF)as the whole frame,and established the decision threshold based on the confidence level of Chi-square distribution.At the same time,the abnormal error judgment value was constructed by Mahalanobis distance function,and the three segments of Huber weight function were formed.It could improve the accuracy of the interval judgment of outliers,and give a reasonable weight,so as to improve the tracking accuracy of the algorithm.The data values of four important locations in the vehicle obtained after optimized filtering were processed by information fusion.According to theoretical analysis,compared with Kalman filtering algorithm,the proposed algorithm could accurately track the actual temperature in the case of abnormal error,and multi-station data fusion processing could improve the overall fault tolerance of the system.The results showed that the proposed algorithm effectively reduced the interference of abnormal errors on filtering,and the synthetic value of fusion processing was more stable and critical.
基金supported in part by the National Key Research and Development Program of China(2023YFB3906403)the National Natural Science Foundation of China(62373118,62173105)the Natural Science Foundation of Heilongjiang Province of China(ZD2023F002)
文摘Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state estimation.Unfortunately, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272123 and 12302047)the Natural Science Foundation of Jiangsu Province(Grant No.BK20231185)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.SJCX24_0192).
文摘The state estimation of the flexible multibody systems is a vital issue since it is the base of effective control and condition monitoring.The research on the state estimation method of flexible multibody system with large deformation and large rotation remains rare.In this investigation,a state estimator based on multiple nonlinear Kalman filtering algorithms was designed for the flexible multibody systems containing large flexibility components that were discretized by absolute nodal coordinate formulation(ANCF).The state variable vector was constructed based on the independent coordinates which are identified through the constraint Jacobian.Three types of Kalman filters were used to compare their performance in the state estimation for ANCF.Three cases including flexible planar rotating beam,flexible four-bar mechanism,and flexible rotating shaft were employed to verify the proposed state estimator.According to the different performances of the three types of Kalman filter,suggestions were given for the construction of the state estimator for the flexible multibody system.
基金Supported by the China’s National Key R&D Program(Grant No.2022YFB2503103).
文摘The tire-road friction coefficient(TRFC)plays a critical role in vehicle safety and dynamic stability,with model-based approaches being the primary method for TRFC estimation.However,the accuracy of these methods is often constrained by the complexity of tire force expressions and uncertainties in tire model parameters,particularly under diverse and complex driving conditions.To address these challenges,this paper proposes a novel data enforced unscented Kalman filter(DeUKF)approach for precise TRFC estimation in intelligent chassis systems.First,an Unscented Kalman Filter is constructed using a nominal tire model-based vehicle dynamics formulation.Then,leveraging Willems’Fundamental Lemma and historical real-world driving data,the vehicle dynamics model is adap-tively corrected within the Unscented Kalman Filter framework.This correction effectively mitigates the adverse effects of tire model uncertainties,thereby enhancing TRFC estimation accuracy.Finally,real vehicle experiments are conducted to validate the effectiveness and superiority of the proposed method.
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 support from National Science and Technology Council,Taiwan under grant numbers NSTC 113-2811-E-019-001 and NSTC 113-2221-E-019-059.
文摘The extended Kalman filter(EKF)is extensively applied in integrated navigation systems that combine the global navigation satellite system(GNSS)and strap-down inertial navigation system(SINS).However,the performance of the EKF can be severely impacted by non-Gaussian noise and measurement noise uncertainties,making it difficult to achieve optimal GNSS/INS integration.Dealing with non-Gaussian noise remains a significant challenge in filter development today.Therefore,the maximum correntropy criterion(MCC)is utilized in EKFs to manage heavytailed measurement noise.However,its capability to handle non-Gaussian process noise and unknown disturbances remains largely unexplored.In this paper,we extend correntropy from using a single kernel to a multi-kernel approach.This leads to the development of a multi-kernel maximum correntropy extended Kalman filter(MKMC-EKF),which is designed to effectively manage multivariate non-Gaussian noise and disturbances.Further,theoretical analysis,including advanced stability proofs,can enhance understanding,while hybrid approaches integrating MKMC-EKF with particle filters may improve performance in nonlinear systems.The MKMC-EKF enhances estimation accuracy using a multi-kernel bandwidth approach.As bandwidth increases,the filter’s sensitivity to non-Gaussian features decreases,and its behavior progressively approximates that of the iterated EKF.The proposed approach for enhancing positioning in navigation is validated through performance evaluations,which demonstrate its practical applications in real-world systems like GPS navigation and measuring radar targets.
基金Supported by the Foreign Experts Project of the Belt and Road Innovative Talent Exchange(No.DL2023016005L).
文摘This article proposes an adaptive extended Kalman filter(EKF)for nonlinear cyber-physical systems(CPSs)under unknown inputs and non-Gaussian noises.It is known that the traditional extended Kalman filter is applicable to nonlinear systems with Gaussian white noise.The system is reformulated with intermediate variables to expand the application of nonlinear systems under unknown inputs and non-Gaussian noises,which help decompose unknown input estimation into residual tracking and state observation subproblems.By introducing the orthogonal principle of innovation and attenuation factor,the intermediate variables-based filter can improve the estimation performance under non-Gaussian noises and unknown inputs.Simulation results validate the effectiveness of the proposed method.
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 in part by Sichuan Science and Technology Program under Grant No.2025ZNSFSC151in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27030201+1 种基金the Natural Science Foundation of China under Grant No.U21B6001in part by the Natural Science Foundation of Tianjin under Grant No.24JCQNJC01930.
文摘The work proposes a distributed Kalman filtering(KF)algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way.We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions,which implies that the theoretical results are able to be applied to stochastic feedback systems.Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation.We employ analysis techniques such as stochastic Lyapunov function,stability theory of stochastic systems,and algebraic graph theory to deal with the above issue.The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal,the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way.At last,we illustrate the property of the proposed distributed KF algorithm by a simulation example.
基金supported by the Foundation of Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology,Ministry of Education,Chinathe National Natural Science Foundation of China (61873064)
文摘Solar radio burst(SRB)is one of the main natural interference sources of Global Positioning System(GPS)signals and can reduce the signal-to-noise ratio(SNR),directly affecting the tracking performance of GPS receivers.In this paper,a tracking algorithm based on the adaptive Kalman filter(AKF)with carrier-to-noise ratio estimation is proposed and compared with the conventional second-order phase-locked loop tracking algo-rithms and the improved Sage-Husa adaptive Kalman filter(SHAKF)algorithm.It is discovered that when the SRBs occur,the improved SHAKF and the AKF with carrier-to-noise ratio estimation enable stable tracking to loop signals.The conven-tional second-order phase-locked loop tracking algorithms fail to track the receiver signal.The standard deviation of the carrier phase error of the AKF with carrier-to-noise ratio estimation out-performs 50.51%of the improved SHAKF algorithm,showing less fluctuation and better stability.The proposed algorithm is proven to show more excellent adaptability in the severe envi-ronment caused by the SRB occurrence and has better tracking performance.
基金Pre-Research Program of General Armament Departmentduring the 11th Five-Year Plan Period(No.51309010201)the National Natural Science Foundation of China(No.60575010)
文摘Using a gravity anomaly covariance function based on the second-order Ganssian Markov gravity anomaly potential model, the state equation of a gravity anomaly signal is obtained in marine gravimetry. Combined with the system state equation and the measurement equation, a new method of the cascade Kalman filter is proposed and applied to the correction of gravity anomaly distortion. In the signal processing procedure, an inverse Kalman filter is used to restore the gravity anomaly signal and high frequency noises first. Then an adaptive Kalman filter, which uses the gravity anomaly state equation as the system equation, is set to estimate the actual gravity anomaly data. Emulations and experiments indicate that both the cascade Kalman filter method and the single inverse Kalman filter method are effective in alleviating the distortion of the gravity anomaly signal, but the performance of the cascade Kalman filter method is better than that of the single inverse Kalman filter method.
基金Pre-Research Program of General Armament Department during the11th Five-Year Plan Period (No51309020503)the National Defense Basic Research Program of China (973Program)(No973-61334)+1 种基金the National Natural Science Foundation of China(No50575042)Specialized Research Fund for the Doctoral Program of Higher Education (No20050286026)
文摘To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.
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.
