This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propos...This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)algorithm.Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space.Subsequently,the original high-dimensional sparse signals can be well recovered by a reconstruction technique.To ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback systems.We further present the performance of the compressed KF algorithm.Specifically,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and optimized.Finally,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.展开更多
A dedicated weak current measurement system was designed to measure the weak currents generated by the neutron ionization chamber.This system incorporates a second-order low-pass filter circuit and the Kalman filterin...A dedicated weak current measurement system was designed to measure the weak currents generated by the neutron ionization chamber.This system incorporates a second-order low-pass filter circuit and the Kalman filtering algorithm to effectively filter out noise and minimize interference in the measurement results.Testing conducted under normal temperature conditions has demonstrated the system's high precision performance.However,it was observed that temperature variations can affect the measurement performance.Data were collected across temperatures ranging from -20 to 70℃,and a temperature correction model was established through linear regression fitting to address this issue.The feasibility of the temperature correction model was confirmed at temperatures of -5 and 40℃,where relative errors remained below 0.1% after applying the temperature correction.The research indicates that the designed measurement system exhibits excellent temperature adaptability and high precision,making it particularly suitable for measuring weak currents.展开更多
Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only desi...Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm (EnKF) assimilation by combing the crop growth model (CERES-Wheat) with remote sensing data, but also optimized and updated the key parameters (LAI) of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.展开更多
For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime(RL),the accuracy and efficiency ...For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime(RL),the accuracy and efficiency of the parameters estimation are not high, and the current degradation state of the target device is not accurately estimated. In this paper, a nonlinear Wiener degradation model with random effect is proposed and the corresponding probability density function(PDF) of the first hitting time(FHT)is deduced. A parameter estimation method based on modified expectation maximum(EM) algorithm is proposed to obtain the estimated value of fixed coefficient and the priori value of random coefficient in the model. The posterior value of the random coefficient and the current degradation state of target device are updated synchronously by the state space model(SSM) and the Kalman filter algorithm. The PDF of RL with random effect is deduced. A simulation example is analyzed to verify that the proposed method has the obvious advantage over the existing methods in parameter estimation error and RL prediction accuracy.展开更多
Background:The accurate estimation of carbon-water flux is critical for understanding the carbon and water cycles of terrestrial ecosystems and further mitigating climate change.Model simulations and observations have...Background:The accurate estimation of carbon-water flux is critical for understanding the carbon and water cycles of terrestrial ecosystems and further mitigating climate change.Model simulations and observations have been widely used to research water and carbon cycles of terrestrial ecosystems.Given the advantages and limitations of each method,combining simulations and observations through a data assimilation technique has been proven to be highly promising for improving carbon-water flux simulation.However,to the best of our knowledge,few studies have accomplished both parameter optimization and the updating of model state variables through data assimilation for carbon-water flux simulation in multiple vegetation types.And little is known about the variation of the performance of data assimilation for carbon-water flux simulation in different vegetation types.Methods:In this study,we assimilated leaf area index(LAI)time-series observations into a biogeochemical model(Biome-BGC)using different assimilation algorithms(ensemble Kalman filter algorithm(EnKF)and unscented Kalman filter(UKF))in different vegetation types(deciduous broad-leaved forest(DBF),evergreen broad-leaved forest(EBF)and grassland(GL))to simulate carbon-water flux.Results:The validation of the results against the eddy covariance measurements indicated that,overall,compared with the original simulation,assimilating the LAI into the Biome-BGC model improved the carbon-water flux simulations(R^(2)increased by 35%,root mean square error decreased by 10%;the sum of the absolute error decreased by 8%)but more significantly,improved the water flux simulations(R^(2)increased by 31%,root mean square error decreased by 18%;the sum of the absolute error decreased by 16%).Among the different forest types,the data assimilation techniques(both EnKF and UKF)achieved the best performance towards carbon-water flux in EBF(R^(2)increased by 44%,root mean square error decreased by 24%;the sum of the absolute error decreased by 28%),and the performances of EnKF and UKF showed slightly different when simulating carbon fluxes.Conclusion:We suggest that to reduce the uncertainty in global carbon-water flux quantification,forthcoming data assimilation treatment should consider the vegetation types where the data assimilation experiments are carried out,the simulated objectives and the assimilation algorithms.展开更多
In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment, the information fusion between an ultrasonic sensor and a binocular sensor was stu...In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment, the information fusion between an ultrasonic sensor and a binocular sensor was studied under the condition that the robot moves in the Walk gait on a structured road. Firstly, the distance information of obstacles from these two sensors was separately processed by the Kalman filter algorithm, which largely reduced the noise interference. After that, we obtained two groups of estimated distance values from the robot to the obstacle and a variance of the estimation value. Additionally, a fusion of the estimation values and the variances was achieved based on the STF fusion algorithm. Finally, a simulation was performed to show that the curve of a real value was tracked well by that of the estimation value, which attributes to the effectiveness of the Kalman filter algorithm. In contrast to statistics before fusion, the fusion variance of the estimation value was sharply decreased. The precision of the position information is 4. 6 cm, which meets the application requirements of the robot.展开更多
Unmanned aerial vehicle(UAV)positioning is one of the key techniques in the field of UAV navigation.Although the high positioning precision of UAV can be achieved through global positioning system(GPS),the frequency o...Unmanned aerial vehicle(UAV)positioning is one of the key techniques in the field of UAV navigation.Although the high positioning precision of UAV can be achieved through global positioning system(GPS),the frequency of updating signal in GPS is low and the energy consumption of GPS module is huge,which does not satisfy the real-time demand of UAV positioning.In this paper,a multi-sensor information fusion method based on GPS,inertial navigation system(INS),and the visible light sensors is proposed for UAV positioning.The Kalman filter combining with simulated annealing algorithm is used to estimate the position error between GPS or INS and the visible light sensors,and then the motion trajectory is corrected according to this position error information.Therefore,the positioning accuracy of UAV can be improved in case of only INS being available.Experimental results demonstrate that the proposed method can remarkably improve the positioning accuracy and greatly reduce the energy consumption.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB3305600)the National Natural Science Foundation of China(Grant Nos.61621003,62141604)+1 种基金the China Postdoctoral Science Foundation(Grant No.2022M722926)the Major Key Project of Peng Cheng Laboratory(Grant No.PCL2023AS1-2)。
文摘This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic systems.To deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)algorithm.Our algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional space.Subsequently,the original high-dimensional sparse signals can be well recovered by a reconstruction technique.To ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback systems.We further present the performance of the compressed KF algorithm.Specifically,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and optimized.Finally,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.
基金supported by the Youth Science Foundation of Sichuan Province(Nos.2022NSFSC1230 and 2022NSFSC1231)the Science and Technology Innovation Seedling Project of Sichuan Province(No.MZGC20230080)+1 种基金the General project of the National Natural Science Foundation of China(No.12075039)the Key project of the National Natural Science Foundation of China(No.U19A2086)。
文摘A dedicated weak current measurement system was designed to measure the weak currents generated by the neutron ionization chamber.This system incorporates a second-order low-pass filter circuit and the Kalman filtering algorithm to effectively filter out noise and minimize interference in the measurement results.Testing conducted under normal temperature conditions has demonstrated the system's high precision performance.However,it was observed that temperature variations can affect the measurement performance.Data were collected across temperatures ranging from -20 to 70℃,and a temperature correction model was established through linear regression fitting to address this issue.The feasibility of the temperature correction model was confirmed at temperatures of -5 and 40℃,where relative errors remained below 0.1% after applying the temperature correction.The research indicates that the designed measurement system exhibits excellent temperature adaptability and high precision,making it particularly suitable for measuring weak currents.
基金supported by the National Natural Science Foundation of China (40701120)the Beijing Natural Science Foundation, China (4092016)the Beijing Nova, China (2008B33)
文摘Data assimilation in agricultural remote sensing research is of great significance to integrate with remote sensing observations and model simulations for parameters estimation. The present investigation not only designed and realized the Ensemble Kalman Filtering algorithm (EnKF) assimilation by combing the crop growth model (CERES-Wheat) with remote sensing data, but also optimized and updated the key parameters (LAI) of winter wheat by using remote sensing data. Results showed that the assimilation LAI and the observation ones agreed with each other, and the R2 reached 0.8315. So assimilation remote sensing and crop model could provide reference data for the agricultural production.
基金supported by the National Defense Foundation of China(71601183)the China Postdoctoral Science Foundation(2017M623415)
文摘For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime(RL),the accuracy and efficiency of the parameters estimation are not high, and the current degradation state of the target device is not accurately estimated. In this paper, a nonlinear Wiener degradation model with random effect is proposed and the corresponding probability density function(PDF) of the first hitting time(FHT)is deduced. A parameter estimation method based on modified expectation maximum(EM) algorithm is proposed to obtain the estimated value of fixed coefficient and the priori value of random coefficient in the model. The posterior value of the random coefficient and the current degradation state of target device are updated synchronously by the state space model(SSM) and the Kalman filter algorithm. The PDF of RL with random effect is deduced. A simulation example is analyzed to verify that the proposed method has the obvious advantage over the existing methods in parameter estimation error and RL prediction accuracy.
基金supported by the National Natural Science Foundation of China(No.41301451).
文摘Background:The accurate estimation of carbon-water flux is critical for understanding the carbon and water cycles of terrestrial ecosystems and further mitigating climate change.Model simulations and observations have been widely used to research water and carbon cycles of terrestrial ecosystems.Given the advantages and limitations of each method,combining simulations and observations through a data assimilation technique has been proven to be highly promising for improving carbon-water flux simulation.However,to the best of our knowledge,few studies have accomplished both parameter optimization and the updating of model state variables through data assimilation for carbon-water flux simulation in multiple vegetation types.And little is known about the variation of the performance of data assimilation for carbon-water flux simulation in different vegetation types.Methods:In this study,we assimilated leaf area index(LAI)time-series observations into a biogeochemical model(Biome-BGC)using different assimilation algorithms(ensemble Kalman filter algorithm(EnKF)and unscented Kalman filter(UKF))in different vegetation types(deciduous broad-leaved forest(DBF),evergreen broad-leaved forest(EBF)and grassland(GL))to simulate carbon-water flux.Results:The validation of the results against the eddy covariance measurements indicated that,overall,compared with the original simulation,assimilating the LAI into the Biome-BGC model improved the carbon-water flux simulations(R^(2)increased by 35%,root mean square error decreased by 10%;the sum of the absolute error decreased by 8%)but more significantly,improved the water flux simulations(R^(2)increased by 31%,root mean square error decreased by 18%;the sum of the absolute error decreased by 16%).Among the different forest types,the data assimilation techniques(both EnKF and UKF)achieved the best performance towards carbon-water flux in EBF(R^(2)increased by 44%,root mean square error decreased by 24%;the sum of the absolute error decreased by 28%),and the performances of EnKF and UKF showed slightly different when simulating carbon fluxes.Conclusion:We suggest that to reduce the uncertainty in global carbon-water flux quantification,forthcoming data assimilation treatment should consider the vegetation types where the data assimilation experiments are carried out,the simulated objectives and the assimilation algorithms.
基金Supported by the Ministerial Level Advanced Research Foundation(40401060305)
文摘In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment, the information fusion between an ultrasonic sensor and a binocular sensor was studied under the condition that the robot moves in the Walk gait on a structured road. Firstly, the distance information of obstacles from these two sensors was separately processed by the Kalman filter algorithm, which largely reduced the noise interference. After that, we obtained two groups of estimated distance values from the robot to the obstacle and a variance of the estimation value. Additionally, a fusion of the estimation values and the variances was achieved based on the STF fusion algorithm. Finally, a simulation was performed to show that the curve of a real value was tracked well by that of the estimation value, which attributes to the effectiveness of the Kalman filter algorithm. In contrast to statistics before fusion, the fusion variance of the estimation value was sharply decreased. The precision of the position information is 4. 6 cm, which meets the application requirements of the robot.
文摘Unmanned aerial vehicle(UAV)positioning is one of the key techniques in the field of UAV navigation.Although the high positioning precision of UAV can be achieved through global positioning system(GPS),the frequency of updating signal in GPS is low and the energy consumption of GPS module is huge,which does not satisfy the real-time demand of UAV positioning.In this paper,a multi-sensor information fusion method based on GPS,inertial navigation system(INS),and the visible light sensors is proposed for UAV positioning.The Kalman filter combining with simulated annealing algorithm is used to estimate the position error between GPS or INS and the visible light sensors,and then the motion trajectory is corrected according to this position error information.Therefore,the positioning accuracy of UAV can be improved in case of only INS being available.Experimental results demonstrate that the proposed method can remarkably improve the positioning accuracy and greatly reduce the energy consumption.