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Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter 被引量:1
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作者 Meng Shen Yan Chen +1 位作者 Pinqiang Wang Weimin Zhang 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2022年第2期26-40,共15页
The Localized Weighted Ensemble Kalman Filter(LWEnKF)is a new nonlinear/non-Gaussian data assimilation(DA)method that can effectively alleviate the filter degradation problem faced by particle filtering,and it has gre... The Localized Weighted Ensemble Kalman Filter(LWEnKF)is a new nonlinear/non-Gaussian data assimilation(DA)method that can effectively alleviate the filter degradation problem faced by particle filtering,and it has great prospects for applications in geophysical models.In terms of operational applications,along-track sea surface height(AT-SSH),swath sea surface temperature(S-SST)and in-situ temperature and salinity(T/S)profiles are assimilated using the LWEnKF in the northern South China Sea(SCS).To adapt to the vertical S-coordinates of the Regional Ocean Modelling System(ROMS),a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF.The results show that the LWEnKF outperforms the local particle filter(LPF)due to the introduction of the Ensemble Kalman Filter(EnKF)as a proposal density;the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF,but the RMSEs of T/S profiles reduce significantly by approximately 55%for the T profile and 35%for the S profile(relative to the EnKF).As a result,the LWEnKF makes more reasonable predictions of the internal ocean temperature field.In addition,the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF. 展开更多
关键词 data assimilation Localized weighted Ensemble Kalman Filter northern South China Sea sea surface height sea surface temperature temperature and salinity profiles mesoscale eddy
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Pyramidal Edge Detection Method Based on AWFM Filtering and Fuzzy Linking Model
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作者 王志刚 WangDong 《High Technology Letters》 EI CAS 2002年第1期26-31,共6页
A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repeti... A novel multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean(AWFM)filtering and fuzzy linking model, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to fine edge detection. The validation experiment results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector andprevious multiresolution approach, especially in impulse noise environment. 展开更多
关键词 Edge detector Pyramidal structure Adaptive weight fuzzy mean filter Fuzzy modeling Impulse noise
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Low and non-uniform illumination color image enhancement using weighted guided image filtering 被引量:6
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作者 Qi Mu Xinyue Wang +1 位作者 Yanyan Wei Zhanli Li 《Computational Visual Media》 EI CSCD 2021年第4期529-546,共18页
In the state of the art,grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination.As these methods are applied to each RGB channel i... In the state of the art,grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination.As these methods are applied to each RGB channel independently,imbalanced inter-channel enhancements(color distortion)can often be observed in the resulting images.On the other hand,images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring,halos,and over-enhancement.To address these problems,an improved RGB color image enhancement method is proposed for images captured under nonuniform illumination or in poor visibility,based on weighted guided image filtering(WGIF).Unlike the conventional retinex algorithm and its variants,WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component;it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization.To limit color distortion,RGB images are first converted to HSI(hue,saturation,intensity)color space,where only the intensity channel is enhanced,before being converted back to RGB space by a linear color restoration algorithm.Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination,with better visual quality and objective evaluation scores than from comparator algorithms.It is also efficient due to use of a linear color restoration algorithm. 展开更多
关键词 color image enhancement non-uniform illumination low illumination weighted guided image filter(WGIF) color restoration
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The statistical observation localized equivalent-weights particle filter in a simple nonlinear model 被引量:2
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作者 Yuxin Zhao Shuo Yang +4 位作者 Renfeng Jia Di Zhou Xiong Deng Chang Liu Xinrong Wu 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2022年第2期80-90,共11页
This paper presents an improved approach based on the equivalent-weights particle filter(EWPF)that uses the proposal density to effectively improve the traditional particle filter.The proposed approach uses historical... This paper presents an improved approach based on the equivalent-weights particle filter(EWPF)that uses the proposal density to effectively improve the traditional particle filter.The proposed approach uses historical data to calculate statistical observations instead of the future observations used in the EWPF’s proposal density and draws on the localization scheme used in the localized PF(LPF)to construct the localized EWPF.The new approach is called the statistical observation localized EWPF(LEWPF-Sobs);it uses statistical observations that are better adapted to the requirements of real-time assimilation and the localization function is used to calculate weights to reduce the effect of missing observations on the weights.This approach not only retains the advantages of the EWPF,but also improves the assimilation quality when using sparse observations.Numerical experiments performed with the Lorenz 96 model show that the statistical observation EWPF is better than the EWPF and EAKF when the model uses standard distribution observations.Comparisons of the statistical observation localized EWPF and LPF reveal the advantages of the new method,with fewer particles giving better results.In particular,the new improved filter performs better than the traditional algorithms when the observation network contains densely spaced measurements associated with model state nonlinearities. 展开更多
关键词 data assimilation particle filter equivalent weights particle filter localization methods
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Frequency Weighting Filter Design for Automotive Ride Comfort Evaluation 被引量:3
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作者 DU Feng 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第4期727-738,共12页
Few study gives guidance to design weighting filters according to the frequency weighting factors,and the additional evaluation method of automotive ride comfort is not made good use of in some countries.Based on the ... Few study gives guidance to design weighting filters according to the frequency weighting factors,and the additional evaluation method of automotive ride comfort is not made good use of in some countries.Based on the regularities of the weighting factors,a method is proposed and the vertical and horizontal weighting filters are developed.The whole frequency range is divided several times into two parts with respective regularity.For each division,a parallel filter constituted by a low-and a high-pass filter with the same cutoff frequency and the quality factor is utilized to achieve section factors.The cascading of these parallel filters obtains entire factors.These filters own a high order.But,low order filters are preferred in some applications.The bilinear transformation method and the least P-norm optimal infinite impulse response(IIR) filter design method are employed to develop low order filters to approximate the weightings in the standard.In addition,with the window method,the linear phase finite impulse response(FIR) filter is designed to keep the signal from distorting and to obtain the staircase weighting.For the same case,the traditional method produces 0.330 7 m · s^–2 weighted root mean square(r.m.s.) acceleration and the filtering method gives 0.311 9 m · s^–2 r.m.s.The fourth order filter for approximation of vertical weighting obtains 0.313 9 m · s^–2 r.m.s.Crest factors of the acceleration signal weighted by the weighting filter and the fourth order filter are 3.002 7 and 3.011 1,respectively.This paper proposes several methods to design frequency weighting filters for automotive ride comfort evaluation,and these developed weighting filters are effective. 展开更多
关键词 frequency weighting ride comfort evaluation least P-norm optimal method bilinear transformation weighting filter design
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A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter
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作者 何彬彬 《Chinese Optics Letters》 SCIE EI CAS CSCD 2007年第6期367-369,共3页
A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) time-series data products based on the gradient inverse weighted filter and... A simple data assimilation method for improving estimation of moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) time-series data products based on the gradient inverse weighted filter and object analysis is proposed. The properties and quality control (QC) of MODIS LAI data products are introduced. Also, the gradient inverse weighted filter and object analysis are analyzed. An experiment based on the simple data assimilation method is performed using MODIS LAI data sets from 2000 to 2005 of Guizhou Province in China. 展开更多
关键词 MODIS data A simple data assimilation method for improving the MODIS LAI time-series data products based on the object analysis and gradient inverse weighted filter LAI time
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Effects of Soil Hydraulic Properties on Soil Moisture Estimation 被引量:1
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作者 Xiaolei FU Haishen LYU +5 位作者 Zhongbo YU Xiaolei JIANG Yongjian DING Donghai ZHENG Jinbai HUANG Hongyuan FANG 《Journal of Meteorological Research》 SCIE CSCD 2023年第1期58-74,共17页
Accurate quantification of soil moisture is essential to understand the land surface processes.Soil hydraulic properties influence water transport in soil and thus affect the estimation of soil moisture.However,some s... Accurate quantification of soil moisture is essential to understand the land surface processes.Soil hydraulic properties influence water transport in soil and thus affect the estimation of soil moisture.However,some soil hydraulic properties are only observable at a few field sites.In this study,the effects of soil hydraulic properties on soil moisture estimation are investigated by using the one-dimensional(1-D)Richards equation at ELBARA,which is part of the Maqu monitoring network over the Tibetan Plateau(TP),China.Soil moisture assimilation experiments are then conducted with the unscented weighted ensemble Kalman filter(UWEnKF).The results show that the soil hydraulic properties significantly affect soil moisture simulation.Saturated soil hydraulic conductivity(Ksat)is optimized based on its observations in each soil layer with a genetic algorithm(GA,a widely used optimization method in hydrology),and the 1-D Richards equation performs well using the optimized values.If the range of Ksat for a complete soil profile is known for a particular soil texture(rather than for arbitrary layers within the horizon),optimized Ksat for each soil layer can be obtained by increasing the number of generations in GA,although this increases the computational cost of optimization.UWEnKF performs well with optimized Ksat,and improves the accuracy of soil moisture simulation more than that with calculated Ksat.Sometimes,better soil moisture estimation can be obtained by using optimized saturated volumetric soil moisture content Ksat.In summary,an accurate soil profile can be obtained by using soil moisture assimilation with optimized soil hydraulic properties. 展开更多
关键词 soil moisture one-dimensional(1-D)Richards equation unscented weighted ensemble Kalman filter(UWEnKF) soil hydraulic properties genetic algorithm
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A New Approach to State Estimation for Uncertain Linear Systems in a Moving Horizon Estimation Setting 被引量:2
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作者 J.Garcia-Tirado H.Botero F.Angulo 《International Journal of Automation and computing》 EI CSCD 2016年第6期653-664,共12页
This paper addresses the state estimation problem for linear systems with additive uncertainties in both the state and output equations using a moving horizon approach. Based on the full information estimation setting... This paper addresses the state estimation problem for linear systems with additive uncertainties in both the state and output equations using a moving horizon approach. Based on the full information estimation setting and the game-theoretic approach to the H∞filtering, a new optimization-based estimation scheme for uncertain linear systems is proposed, namely the H∞-full information estimator, H∞-FIE in short. In this formulation, the set of processed data grows with time as more measurements are received preventing recursive formulations as in Kalman filtering. To overcome the latter problem, a moving horizon approximation to the H∞-FIE is also presented, the H∞-MHE in short. This moving horizon approximation is achieved since the arrival cost is suitably defined for the proposed scheme. Sufficient conditions for the stability of the H∞-MHE are derived. Simulation results show the benefits of the proposed scheme when compared with two H∞filters and the well-known Kalman filter. 展开更多
关键词 uncertain processed overcome estimator latter horizon filtering recursive weighting constraints
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