Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problem...Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.展开更多
To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satell...To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.展开更多
Based on the characteristics of impulse noises, the authors establish a new filter, Iterative Adaptive Median Filter （IAMF）. Acccording to the characteristics of images polluted by impulse noises, they establish wei...Based on the characteristics of impulse noises, the authors establish a new filter, Iterative Adaptive Median Filter （IAMF）. Acccording to the characteristics of images polluted by impulse noises, they establish weight function combined with iterative algorithm to eliminate noises. In IAMF filter process, because the noise sixes do not participate in the computation, they do not influence the normal points in the image, therefore IAMF can retain the detail well, maintain the good clarity after processing image, and simultaneously reduce the computation. Experiments showed that IAMF have ideal denoising effect for the images polluted by the impulse noises; especially when the noise rates are more than 0.5, IAMF is mote prominent, even when the noise rotes are more than 0.9, IAMF can achieve a satisfactory results.展开更多
How to deal with colored noises of GOCE （Gravity field and steady - state Ocean Circulation Explorer） satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the...How to deal with colored noises of GOCE （Gravity field and steady - state Ocean Circulation Explorer） satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis. According to the analysis results, gravity field model of the optima] degrees 90-240 is given, which is recovered by COCE gradient data. This paper presents an iterative Wiener filtering method based on the gravity gradient invariants. By this method a degree-220 model was calculated from GOCE SGG （Satellite Gravity Gradient） data. The degrees above 90 of ITG2010 were taken as the prior gravity field model, replacing the low degree gravity field model calculated by GOCE orbit data. GOCE gradient colored noises was processed by Wiener filtering. Finally by Wiener filtering iterative calculation, the gravity field model was restored by space-wise harmonic analysis method. The results show that the model＇s accuracy matched well with the ESA＇s （European Space Agency） results by using the same data,展开更多
A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG tempe...A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.展开更多
This paper addresses the problem of channel estimation in 5G-enabled vehicular-to-vehicular(V2V) channels with high-mobility environments and non-stationary feature. Considering orthogonal frequency division multiplex...This paper addresses the problem of channel estimation in 5G-enabled vehicular-to-vehicular(V2V) channels with high-mobility environments and non-stationary feature. Considering orthogonal frequency division multiplexing(OFDM) system, we perform extended Kalman filter(EKF) for channel estimation in conjunction with Iterative Detector & Decoder(IDD) at the receiver to improve the estimation accuracy. The EKF is proposed for jointly estimating the channel frequency response and the time-varying time correlation coefficients. And the IDD structure is adopted to reduce the estimation errors in EKF. The simulation results show that, compared with traditional methods, the proposed method effectively promotes the system performance.展开更多
For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping （SLAM）, a novel SLAM based on improved Rao-Blackwellized H∞ particle filter （IRBHF-SLAM...For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping （SLAM）, a novel SLAM based on improved Rao-Blackwellized H∞ particle filter （IRBHF-SLAM） algorithm is proposed. The iterated unscented H∞ filter （IUHF） is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor＇s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM （UFastSLAM） under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system （ROS）, runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX （URG） laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional （2-D） grid-map with high precision in different environments.展开更多
In this paper, a new passive modified iterated extended Kalman filter （MIEKF） using the combined set of beatings and frequency measurements in Earth Centered Inertial （ECI） coordinate is proposed. A new measuremen...In this paper, a new passive modified iterated extended Kalman filter （MIEKF） using the combined set of beatings and frequency measurements in Earth Centered Inertial （ECI） coordinate is proposed. A new measurement update equation of MIEKF is derived by modifying the objective function of the Gauss-Newton iteration. A new gain equation and iteration termination criteria are acquired by applying the property of the maximum likelihood estimate. The approximated second order linearized state propagation equation, Jacobian matrix of state transfer and measurement equations are derived in satellite two-body movement. The tracking performances of MIEKF, iterated extended Kalman filter （IEKF） and extended Kalman filter （EKF） are compared via Monte Carlo simulations through simulated data from STK8.1. Simulation results indicate that the proposed MIEKF is possible to passively track low earth circular orbit satellite by a high earth orbit satellite, and has higher tracking precision than the IEKF and EKF.展开更多
基金support from the Shandong Natural Science Foundation(Grant No.ZR2010EM053)the Fundamental Research Funds for the Central Universities(Grant No.10CX04042A)
文摘Reservoir inversion by production history matching is an important way to decrease the uncertainty of the reservoir description. Ensemble Kalman filter （EnKF） is a new data assimilation method. There are two problems have to be solved for the standard EnKF. One is the inconsistency between the updated model and the updated dynamical variables for nonlinear problems, another is the filter divergence caused by the small ensemble size. We improved the EnKF to overcome these two problems. We use the half iterative EnKF （HIEnKF） for reservoir inversion by doing history matching. During the H1EnKF process, the prediction data are obtained by rerunning the reservoir simulator using the updated model. This can guarantee that the updated dynamical variables are consistent with the updated model. The updated model can nonlinearly affect the prediction data. It is proved that HIEnKF is similar to the first iteration of the EnRML method. Covariance localization is introduced to alleviate filter divergence and spurious correlations caused by the small ensemble size. By defining the shape and size of the correlation area, spurious correlation between the gridblocks far apart is alleviated. More freedom of the model ensemble is preserved. The results of history matching and inverse problem obtained from the HIEnKF with covariance localization are improved. The results show that the model freedom increases with a decrease in the correlation length. Therefore the production data can be matched better. But too small a correlation length can lose some reservoir information and this would cause big errors in the reservoir model estimation.
基金supported in part by the Shandong Natural Science Foundation under Grant ZR2020MF067.
文摘To provide stable and accurate position information of control points in a complex coastal environment,an adaptive iterated extended Kalman filter(AIEKF)for fixed-point positioning integrating global navigation satellite system,inertial navigation system,and ultra wide band(UWB)is proposed.In thismethod,the switched global navigation satellite system(GNSS)and UWB measurement are used as the measurement of the proposed filter.For the data fusion filter,the expectation-maximization(EM)based IEKF is used as the forward filter,then,the Rauch-Tung-Striebel smoother for IEKF filter’s result smoothing.Tests illustrate that the proposed AIEKF is able to provide an accurate estimation.
基金supported by Shandong Prvince Natural Science Foundation(Y2008G31)
文摘Based on the characteristics of impulse noises, the authors establish a new filter, Iterative Adaptive Median Filter （IAMF）. Acccording to the characteristics of images polluted by impulse noises, they establish weight function combined with iterative algorithm to eliminate noises. In IAMF filter process, because the noise sixes do not participate in the computation, they do not influence the normal points in the image, therefore IAMF can retain the detail well, maintain the good clarity after processing image, and simultaneously reduce the computation. Experiments showed that IAMF have ideal denoising effect for the images polluted by the impulse noises; especially when the noise rates are more than 0.5, IAMF is mote prominent, even when the noise rotes are more than 0.9, IAMF can achieve a satisfactory results.
基金supported by the National Natural Science Foundation of China(41404020)
文摘How to deal with colored noises of GOCE （Gravity field and steady - state Ocean Circulation Explorer） satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis. According to the analysis results, gravity field model of the optima] degrees 90-240 is given, which is recovered by COCE gradient data. This paper presents an iterative Wiener filtering method based on the gravity gradient invariants. By this method a degree-220 model was calculated from GOCE SGG （Satellite Gravity Gradient） data. The degrees above 90 of ITG2010 were taken as the prior gravity field model, replacing the low degree gravity field model calculated by GOCE orbit data. GOCE gradient colored noises was processed by Wiener filtering. Finally by Wiener filtering iterative calculation, the gravity field model was restored by space-wise harmonic analysis method. The results show that the model＇s accuracy matched well with the ESA＇s （European Space Agency） results by using the same data,
基金supported by the National Natural Science Foundation of China(6110418440904018)+3 种基金the National Key Scientific Instrument and Equipment Development Project(2011YQ12004502)the Research Foundation of General Armament Department(201300000008)the Doctor Innovation Fund of Naval University of Engineering(HGBSCXJJ2011008)the Youth Natural Science Foundation of Naval University of Engineering(HGDQNJJ12028)
文摘A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.
基金supported by the National Natural Science Foundation of China (No.61501066,No.61572088,No.61701063)Chongqing Frontier and Applied Basic Research Project (No.cstc2015jcyjA40003,No.cstc2017jcyjAX0026,No.cstc2016jcyjA0209)+1 种基金the Open Fund of the State Key Laboratory of Integrated Services Networks (No.ISN16-03)the Fundamental Research Funds for the Central Universities (No.106112017CDJXY 500001)
文摘This paper addresses the problem of channel estimation in 5G-enabled vehicular-to-vehicular(V2V) channels with high-mobility environments and non-stationary feature. Considering orthogonal frequency division multiplexing(OFDM) system, we perform extended Kalman filter(EKF) for channel estimation in conjunction with Iterative Detector & Decoder(IDD) at the receiver to improve the estimation accuracy. The EKF is proposed for jointly estimating the channel frequency response and the time-varying time correlation coefficients. And the IDD structure is adopted to reduce the estimation errors in EKF. The simulation results show that, compared with traditional methods, the proposed method effectively promotes the system performance.
基金supported by the Scientific and Technological Research Project Funds of Chongqing Municipal Education Commission (KJ130512)the Project Funds of Chongqing Science and Technology Commission (cstc2015jcyj B0241)
文摘For the problems of estimation accuracy, inconsistencies and robustness in mobile robot simultaneous localization and mapping （SLAM）, a novel SLAM based on improved Rao-Blackwellized H∞ particle filter （IRBHF-SLAM） algorithm is proposed. The iterated unscented H∞ filter （IUHF） is utilized to accurately calculate the importance density function, repeatedly correcting the state mean and the covariance matrix by the iterative update method. The laser sensor＇s observation information is introduced into sequential importance sampling routine. It can avoid the calculation of Jacobian matrix and linearization error accumulation; meanwhile, the robustness of the algorithm is enhanced. IRBHF-SLAM is compared with FastSLAM2.0 and the unscented FastSLAM （UFastSLAM） under different noises in simulation experiments. Results show the algorithm can improve the estimation accuracy and stability. The improved approach, based on the robot operation system （ROS）, runs on the Pioneer3-DX robot equipped with a HOKUYO URG-04LX （URG） laser range finder. Experimental results show the improved algorithm can reduce the required number of particles and the operating time; and create online 2 dimensional （2-D） grid-map with high precision in different environments.
基金partly supported by the National Natural Science Foundation of China(No.61104196)the China Specialized Research Fund for the Doctoral Program of Higher Education(No.200802881017)+1 种基金Nanjing University of Science and Technology Research Funding(No.2010ZYTS051)the 'Zijin star' Research Funding(No.AB41381)
文摘In this paper, a new passive modified iterated extended Kalman filter （MIEKF） using the combined set of beatings and frequency measurements in Earth Centered Inertial （ECI） coordinate is proposed. A new measurement update equation of MIEKF is derived by modifying the objective function of the Gauss-Newton iteration. A new gain equation and iteration termination criteria are acquired by applying the property of the maximum likelihood estimate. The approximated second order linearized state propagation equation, Jacobian matrix of state transfer and measurement equations are derived in satellite two-body movement. The tracking performances of MIEKF, iterated extended Kalman filter （IEKF） and extended Kalman filter （EKF） are compared via Monte Carlo simulations through simulated data from STK8.1. Simulation results indicate that the proposed MIEKF is possible to passively track low earth circular orbit satellite by a high earth orbit satellite, and has higher tracking precision than the IEKF and EKF.
