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
In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenario...In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers.The proposed design modifies the extended Kalman filter(EKF)for the global navigation satellite system(GNSS),integrating the maximum correntropy criterion(MCC)and the variational Bayesian(VB)method.This adaptive algorithm effectively reduces non-line-of-sight(NLOS)reception contamination and improves estimation accuracy,particularly in time-varying GNSS measurements.Experimental results show that the proposed method significantly outperforms conventional approaches in estimation accuracy under heavy-tailed outliers and non-Gaussian noise.By combining MCC with VB approximation for real-time noise covariance estimation using fixed-point iteration,the VBMCEKF achieves superior filtering performance in challenging GNSS conditions.The method’s adaptability and precision make it ideal for improving satellite navigation performance in stochastic environments.展开更多
This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities.The stochastic nonlinearities,which take th...This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities.The stochastic nonlinearities,which take the form of statemultiplicative noises,are introduced in systems to describe the phenomenon of nonlinear disturbances.To resist non-Gaussian noises,we consider a new performance index called maximum correntropy criterion(MCC)which describes the similarity between two stochastic variables.To enhance the“robustness”of the kernel parameter selection on the resultant filtering performance,the Cauchy kernel function is adopted to calculate the corresponding correntropy.The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate.By taking advantage of an upper bound on the one-step prediction error covariance,a modified MCC-based performance index is constructed.Subsequently,with the assistance of a fixed-point theorem,the filter gain is obtained by maximizing the proposed cost function.In addition,a sufficient condition is deduced to ensure the uniqueness of the fixed point.Finally,the validity of the filtering method is tested by simulating a numerical example.展开更多
This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the ...This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.展开更多
This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice fo...This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noise was formulated. However, this assumption may not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.展开更多
Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy cri...Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization requirements.Particularly in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF.The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC.Subsequently,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations.Moreover,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation.Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.展开更多
In this paper,the newly-derived maximum correntropy Kalman filter(MCKF)is re-derived from the M-estimation perspective,where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel functi...In this paper,the newly-derived maximum correntropy Kalman filter(MCKF)is re-derived from the M-estimation perspective,where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions.Based on the derivation process,a unified form for the robust Gaussian filters(RGF)based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement.The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals.Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust antijamming performance.展开更多
This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion,due to its robustness against non-Gaussian errors.It includes the temporal aspects on the estimatio...This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion,due to its robustness against non-Gaussian errors.It includes the temporal aspects on the estimation process within a maximum-correntropy-based extended Kalman filter(MCEKF),which is able to deal with both nonlinear supervisory control and data acquisition(SCADA)and phasor measurement unit(PMU)measurement models.By representing the behavior of the state variables with a nonparametric model within the kernel density estimation,it is possible to include abrupt state transitions as part of the process noise with non-Gaussian characteristics.Also,a novel strategy to update the size of Parzen windows in the kernel estimation is proposed to suppress the effects of suspect samples.By properly adjusting the kernel bandwidth,the proposed MCEKF keeps its accuracy during sudden load changes and contingencies,or in the presence of bad data.Simulations with IEEE test systems and the Brazilian interconnected system are carried out.The results show that the method deals with non-Gaussian noises in both the process and measurement,and provides accurate estimates of the system state under normal and abnormal conditions.展开更多
The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filt...The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security.Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.展开更多
Axle box bearings serve as crucial components within the transmission system of high-speed trains.Their failure can directly impact the operational safety of these trains.Accurately determining the dynamic load experi...Axle box bearings serve as crucial components within the transmission system of high-speed trains.Their failure can directly impact the operational safety of these trains.Accurately determining the dynamic load experienced by bearings during the operation of high-speed trains can provide valuable boundary inputs for the study of bearing fatigue life and service performance,thereby holding significant engineering implications.In this study,we propose a high-speed train axle box bearing load estimation method(FMCC-DKF).This method is founded on the Kalman filtering technique of the Maximum Correntropy Criterion(MCC)and employs dummy measurement technology to enhance the stability of estimated loads.We develop a kernel size update algorithm to address the challenges associated with obtaining the key parameter,kernel size of MCC.Comparative analysis of the vertical and lateral loads of the axle box bearing obtained using FMCC-DKF,DKF,and AMCC-DKF,under both measurement noise-free and non-Gaussian noise conditions,is conducted to demonstrate the superiority of the proposed estimation method.The results indicate that the proposed FMCC-DKF method exhibits high estimation accuracy under both measurement noise-free and non-Gaussian noise interference,and maintains its high estimation accuracy despite changes in train speed.The proposed load estimation method demonstrates reliable performance within the low-frequency domain below 70 Hz.展开更多
To tackle the challenge where existing estimation algorithms exhibit performance deterioration or complete failure in polar environments due to impulsive noise,this paper presents a robust orthogonal matching pursuit(...To tackle the challenge where existing estimation algorithms exhibit performance deterioration or complete failure in polar environments due to impulsive noise,this paper presents a robust orthogonal matching pursuit(OMP)algorithm.Firstly,accurate selection of atom bases is achieved by introducing the maximum correntropy criterion(MCC).Secondly,the L_(1) norm is utilized to reconstruct the loss function,mitigating the influence of impulse noise on parameter estimation.Simultaneously,the alternating direction method of multipliers(ADMM)is employed to efficiently obtain the global optimal solution.Numerical simulations and the processing of experimental data collected from the 9th Chinese National Arctic Research Expedition have shown that the proposed method exhibits significant performance improvements compared to classical algorithms.Specifically,it exhibits higher estimation accuracy and stronger robustness under impulsive noise conditions.展开更多
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 National Science and Technology Council,Taiwan under grants NSTC 111-2221-E-019-047 and NSTC 112-2221-E-019-030.
文摘In this paper,an advanced satellite navigation filter design,referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter(VBMCEKF),is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers.The proposed design modifies the extended Kalman filter(EKF)for the global navigation satellite system(GNSS),integrating the maximum correntropy criterion(MCC)and the variational Bayesian(VB)method.This adaptive algorithm effectively reduces non-line-of-sight(NLOS)reception contamination and improves estimation accuracy,particularly in time-varying GNSS measurements.Experimental results show that the proposed method significantly outperforms conventional approaches in estimation accuracy under heavy-tailed outliers and non-Gaussian noise.By combining MCC with VB approximation for real-time noise covariance estimation using fixed-point iteration,the VBMCEKF achieves superior filtering performance in challenging GNSS conditions.The method’s adaptability and precision make it ideal for improving satellite navigation performance in stochastic environments.
基金supported in part by the National Natural Science Foundation of China(62273088,62273087)the Shanghai Pujiang Program of China(22PJ1400400)the Program of Shanghai Academic/Technology Research Leader(20XD1420100)。
文摘This paper tackles the maximum correntropy Kalman filtering problem for discrete time-varying non-Gaussian systems subject to state saturations and stochastic nonlinearities.The stochastic nonlinearities,which take the form of statemultiplicative noises,are introduced in systems to describe the phenomenon of nonlinear disturbances.To resist non-Gaussian noises,we consider a new performance index called maximum correntropy criterion(MCC)which describes the similarity between two stochastic variables.To enhance the“robustness”of the kernel parameter selection on the resultant filtering performance,the Cauchy kernel function is adopted to calculate the corresponding correntropy.The goal of this paper is to design a Kalman-type filter for the underlying systems via maximizing the correntropy between the system state and its estimate.By taking advantage of an upper bound on the one-step prediction error covariance,a modified MCC-based performance index is constructed.Subsequently,with the assistance of a fixed-point theorem,the filter gain is obtained by maximizing the proposed cost function.In addition,a sufficient condition is deduced to ensure the uniqueness of the fixed point.Finally,the validity of the filtering method is tested by simulating a numerical example.
基金Rahul Radhakrishnan received the B.Tech.degree in Applied Electronics and Instrumentation from the Government Engineering College,Calicut,India,in 2010 and the M.Tech.degreein Control Systems from the Department of Electrical Engineering,National Institute of Technology Kurukshetra,India,in 2013.He received the Ph.D.degree from the Department of Electrical Engineering,Indian Institute of Technology Patna,India,in 2018.Currently,he is workingasan Assistant Professor in the Department of Electrical Engineering,Sardar Vallabhbhai National Institute of Technology,Surat,Gujarat,India.His main research interests include nonlinear filtering,aerospace,and underwater target tracking.
文摘This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed.Here,the uncertainties in process and measurements are assumed non-Gaussian,such that the maximum correntropy criterion(MCC)is chosen to replace the conventional minimum mean square error criterion.Furthermore,the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function.Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.
文摘This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noise was formulated. However, this assumption may not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.
基金supported by the National Natural Science Foundation of China under Grant Nos.62273083 and 61803077Natural Science Foundation of Hebei Province under Grant No.F2020501012.
文摘Indoor positioning is a key technology in today’s intelligent environments,and it plays a crucial role in many application areas.This paper proposed an unscented Kalman filter(UKF)based on the maximum correntropy criterion(MCC)instead of the minimummean square error criterion(MMSE).This innovative approach is applied to the loose coupling of the Inertial Navigation System(INS)and Ultra-Wideband(UWB).By introducing the maximum correntropy criterion,the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise,thus enhancing its adaptability to diverse environmental localization requirements.Particularly in the presence of non-Gaussian noise,especially heavy-tailed noise,the MCCUKF exhibits superior accuracy and robustness compared to the traditional UKF.The method initially generates an estimate of the predicted state and covariance matrix through the unscented transform(UT)and then recharacterizes the measurement information using a nonlinear regression method at the cost of theMCC.Subsequently,the state and covariance matrices of the filter are updated by employing the unscented transformation on the measurement equations.Moreover,to mitigate the influence of non-line-of-sight(NLOS)errors positioning accuracy,this paper proposes a k-medoid clustering algorithm based on bisection k-means(Bikmeans).This algorithm preprocesses the UWB distance measurements to yield a more precise position estimation.Simulation results demonstrate that MCCUKF is robust to the uncertainty of UWB and realizes stable integration of INS and UWB systems.
基金supported by the Basic Science Center Program of the National Natural Science Foundation of China(62388101)the National Natural Science Foundation of China(61873275).
文摘In this paper,the newly-derived maximum correntropy Kalman filter(MCKF)is re-derived from the M-estimation perspective,where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions.Based on the derivation process,a unified form for the robust Gaussian filters(RGF)based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement.The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals.Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust antijamming performance.
基金supported by CNPq(No.308297/2018-0)CAPES and FAPESP(No.2016/19646-6)+1 种基金ERDF(COMPETE2020 Programme)FCT(POCI-01-0145-FEDER-016731 INFUSE)
文摘This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion,due to its robustness against non-Gaussian errors.It includes the temporal aspects on the estimation process within a maximum-correntropy-based extended Kalman filter(MCEKF),which is able to deal with both nonlinear supervisory control and data acquisition(SCADA)and phasor measurement unit(PMU)measurement models.By representing the behavior of the state variables with a nonparametric model within the kernel density estimation,it is possible to include abrupt state transitions as part of the process noise with non-Gaussian characteristics.Also,a novel strategy to update the size of Parzen windows in the kernel estimation is proposed to suppress the effects of suspect samples.By properly adjusting the kernel bandwidth,the proposed MCEKF keeps its accuracy during sudden load changes and contingencies,or in the presence of bad data.Simulations with IEEE test systems and the Brazilian interconnected system are carried out.The results show that the method deals with non-Gaussian noises in both the process and measurement,and provides accurate estimates of the system state under normal and abnormal conditions.
基金supported in part by the National Key R&D Program of China (2022ZD0116401,2022ZD0116400)the National Natural Science Foundation of China (62203016,U2241214,T2121002,62373008,61933007)+2 种基金the China Postdoctoral Science Foundation (2021TQ0009)the Royal Society of the UKthe Alexander von Humboldt Foundation of Germany。
文摘The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical significance.The main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber security.Finally, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.
基金National Key R&D Program of China(Grant numbers 2022YFB4301201-11,2022YFB4301203-05)National Natural Science Foundation of China(Grant number 52202464).
文摘Axle box bearings serve as crucial components within the transmission system of high-speed trains.Their failure can directly impact the operational safety of these trains.Accurately determining the dynamic load experienced by bearings during the operation of high-speed trains can provide valuable boundary inputs for the study of bearing fatigue life and service performance,thereby holding significant engineering implications.In this study,we propose a high-speed train axle box bearing load estimation method(FMCC-DKF).This method is founded on the Kalman filtering technique of the Maximum Correntropy Criterion(MCC)and employs dummy measurement technology to enhance the stability of estimated loads.We develop a kernel size update algorithm to address the challenges associated with obtaining the key parameter,kernel size of MCC.Comparative analysis of the vertical and lateral loads of the axle box bearing obtained using FMCC-DKF,DKF,and AMCC-DKF,under both measurement noise-free and non-Gaussian noise conditions,is conducted to demonstrate the superiority of the proposed estimation method.The results indicate that the proposed FMCC-DKF method exhibits high estimation accuracy under both measurement noise-free and non-Gaussian noise interference,and maintains its high estimation accuracy despite changes in train speed.The proposed load estimation method demonstrates reliable performance within the low-frequency domain below 70 Hz.
基金supported by the National Key Research and Development Program of China(2021YFC2801204)the National Natural Science Foundation of China(62127801)the Stably Supported Project of Key Laboratory of Underwater Acoustics Technology(JCKYS2022604SSJS001).
文摘To tackle the challenge where existing estimation algorithms exhibit performance deterioration or complete failure in polar environments due to impulsive noise,this paper presents a robust orthogonal matching pursuit(OMP)algorithm.Firstly,accurate selection of atom bases is achieved by introducing the maximum correntropy criterion(MCC).Secondly,the L_(1) norm is utilized to reconstruct the loss function,mitigating the influence of impulse noise on parameter estimation.Simultaneously,the alternating direction method of multipliers(ADMM)is employed to efficiently obtain the global optimal solution.Numerical simulations and the processing of experimental data collected from the 9th Chinese National Arctic Research Expedition have shown that the proposed method exhibits significant performance improvements compared to classical algorithms.Specifically,it exhibits higher estimation accuracy and stronger robustness under impulsive noise conditions.
