A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,accordin...A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,according to the empirical forecasting method,guidance forecasts were established for the northern,central,and southern parts of Nanchang City.After inspection,it was found that the temperature prediction model established based on the Kalman filter method in Nanchang station had good prediction performance,and especially in the 24-hour forecast,it had advantages over the European Center.The accuracy of low temperature forecast was better than that of high temperature forecast.展开更多
Software alignment is quite important for a tracking detector to reach its ultimate position accuracy and momentum resolution. We developed a new alignment algorithm for the BESⅢ Main Drift Chamber using the Kalman F...Software alignment is quite important for a tracking detector to reach its ultimate position accuracy and momentum resolution. We developed a new alignment algorithm for the BESⅢ Main Drift Chamber using the Kalman Filter method. Two different types of data which are helix tracks and straight tracks are used to test this algorithm, and the results show that the design and implementation is successful.展开更多
A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely squ...A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely square root generalized cubature quadrature Kalman filter (SR-GCQKF) and iterated generalized cubature quadrature Kalman filter (I-GCQKF). In SR-GCQKF, the QR decomposition is exploited to alter the Cholesky decomposition and both predicted and filtered error covariances have been propagated in square root format to make sure the numerical stability. In I-GCQKF, the measurement update step is executed iteratively to make full use of the latest measurement and a new terminal criterion is adopted to guarantee the increase of likelihood. Detailed numerical experiments demonstrate the superior performance on both tracking stability and estimation accuracy of I-GCQKF and SR-GCQKF compared with GCQKF.展开更多
This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assim...This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme （E4DVAR） benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence： the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.展开更多
Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than th...Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probabifity distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.展开更多
A kind of adaptive color noise Kalman filtering approach based on the correlative method of the system output is proposed to solve the cephalometric images of stomatology. This approach builds the color noise Kalman f...A kind of adaptive color noise Kalman filtering approach based on the correlative method of the system output is proposed to solve the cephalometric images of stomatology. This approach builds the color noise Kalman filtering model by adopting the equivalent measurement equation in order to aviod complicated computation and expansion of the dimension of the filter. It is also unnecessary to know the variance of measurement noise beforehand so that it is closer to the actual situation. The results of several experiments are presented to demonstrate the feasibility and good performance of this approach.展开更多
The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences base...The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.展开更多
In this paper, we present a new approach (Kalman Filter Smoothing) to estimate and forecast survival of Diabetic and Non Diabetic Coronary Artery Bypass Graft Surgery (CABG) patients. Survival proportions of the patie...In this paper, we present a new approach (Kalman Filter Smoothing) to estimate and forecast survival of Diabetic and Non Diabetic Coronary Artery Bypass Graft Surgery (CABG) patients. Survival proportions of the patients are obtained from a lifetime representing parametric model (Weibull distribution with Kalman Filter approach). Moreover, an approach of complete population (CP) from its incomplete population (IP) of the patients with 12 years observations/follow-up is used for their survival analysis [1]. The survival proportions of the CP obtained from Kaplan Meier method are used as observed values yt?at time t (input) for Kalman Filter Smoothing process to update time varying parameters. In case of CP, the term representing censored observations may be dropped from likelihood function of the distribution. Maximum likelihood method, in-conjunction with Davidon-Fletcher-Powell (DFP) optimization method [2] and Cubic Interpolation method is used in estimation of the survivor’s proportions. The estimated and forecasted survival proportions of CP of the Diabetic and Non Diabetic CABG patients from the Kalman Filter Smoothing approach are presented in terms of statistics, survival curves, discussion and conclusion.展开更多
Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF...Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.展开更多
文摘A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,according to the empirical forecasting method,guidance forecasts were established for the northern,central,and southern parts of Nanchang City.After inspection,it was found that the temperature prediction model established based on the Kalman filter method in Nanchang station had good prediction performance,and especially in the 24-hour forecast,it had advantages over the European Center.The accuracy of low temperature forecast was better than that of high temperature forecast.
基金Supported by CAS Knowledge Innovation Project (U-602, U-34)National Natural Science Foundation of China (10491300,10491303, 10605030)100 Talents Program of CAS (U-25, U-54)
文摘Software alignment is quite important for a tracking detector to reach its ultimate position accuracy and momentum resolution. We developed a new alignment algorithm for the BESⅢ Main Drift Chamber using the Kalman Filter method. Two different types of data which are helix tracks and straight tracks are used to test this algorithm, and the results show that the design and implementation is successful.
基金supported by the National Natural Science Foundation of China(6147322711472222)+2 种基金the Aerospace Technology Support Fund of China(2014-HT-XGD)the Natural Science Foundation of Shaanxi Province(2015JM6304)the Aeronautical Science Foundation of China(20151353018)
文摘A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely square root generalized cubature quadrature Kalman filter (SR-GCQKF) and iterated generalized cubature quadrature Kalman filter (I-GCQKF). In SR-GCQKF, the QR decomposition is exploited to alter the Cholesky decomposition and both predicted and filtered error covariances have been propagated in square root format to make sure the numerical stability. In I-GCQKF, the measurement update step is executed iteratively to make full use of the latest measurement and a new terminal criterion is adopted to guarantee the increase of likelihood. Detailed numerical experiments demonstrate the superior performance on both tracking stability and estimation accuracy of I-GCQKF and SR-GCQKF compared with GCQKF.
基金sponsored by the U.S. National Science Foundation (Grant No.ATM0205599)the U.S. Offce of Navy Research under Grant N000140410471Dr. James A. Hansen was partially supported by US Offce of Naval Research (Grant No. N00014-06-1-0500)
文摘This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme （E4DVAR） benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence： the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.
文摘Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probabifity distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.
基金Supported by the High Technology Research and Development Programme of China
文摘A kind of adaptive color noise Kalman filtering approach based on the correlative method of the system output is proposed to solve the cephalometric images of stomatology. This approach builds the color noise Kalman filtering model by adopting the equivalent measurement equation in order to aviod complicated computation and expansion of the dimension of the filter. It is also unnecessary to know the variance of measurement noise beforehand so that it is closer to the actual situation. The results of several experiments are presented to demonstrate the feasibility and good performance of this approach.
文摘The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.
文摘In this paper, we present a new approach (Kalman Filter Smoothing) to estimate and forecast survival of Diabetic and Non Diabetic Coronary Artery Bypass Graft Surgery (CABG) patients. Survival proportions of the patients are obtained from a lifetime representing parametric model (Weibull distribution with Kalman Filter approach). Moreover, an approach of complete population (CP) from its incomplete population (IP) of the patients with 12 years observations/follow-up is used for their survival analysis [1]. The survival proportions of the CP obtained from Kaplan Meier method are used as observed values yt?at time t (input) for Kalman Filter Smoothing process to update time varying parameters. In case of CP, the term representing censored observations may be dropped from likelihood function of the distribution. Maximum likelihood method, in-conjunction with Davidon-Fletcher-Powell (DFP) optimization method [2] and Cubic Interpolation method is used in estimation of the survivor’s proportions. The estimated and forecasted survival proportions of CP of the Diabetic and Non Diabetic CABG patients from the Kalman Filter Smoothing approach are presented in terms of statistics, survival curves, discussion and conclusion.
基金supported by the National Natural Science Foundation of China(7092100160574058)+1 种基金the Key International Cooperation Programs of Hunan Provincial Science & Technology Department (2009WK2009)the General Program of Hunan Provincial Education Department(11C0023)
文摘Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.
