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A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Geodesy and Geodynamics 2024年第1期15卷 100-105页

作者： Fei Ye Yunbin Yuan State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430077China BeiDou High-precision Satellite Navigation and Location Service Hunan Engineering Research Center Hunan Institute of Geomatics Sciences and TechnologyChangsha 410007China Hunan Continuously Operating Reference Stations(HNCORS)Data Center Hunan Institute of Geomatics Sciences and TechnologyChangsha 410007China

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame ***,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM ***,the weighted LS+AR hybrid method performs well for PM short-term ***,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR *** this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid *** using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid *** to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,***,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

关键词： Stochastic model LS+AR Short-term prediction The earth rotation parameter(ERP) observation model

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Remote sensing image fusion based on Bayesian linear estimation

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Science in China(Series F) 2007年第2期50卷 227-240页

作者： GE ZhiRong WANG Bin ZHANG LiMing Department of Electronics Engineering Fudan University Shanghai 200433 China Key Laboratory of Wave Scattering and Remote Sensing Information (Ministry of Education) Fudan University Shanghai 200433 China

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation （BLE） is applied to observation models between remote sensing images with different spatial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral （MS） images, instead of assuming the joint distribution between the panchromatic （PAN） image and low-resolution mulUspectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis （PCA） method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM＋ demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.

关键词： image fusion Bayesian linear estimation observation model Landsat ETM+

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维普期刊数据库

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Robust Object Tracking under Appearance Change Conditions

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International Journal of Automation and computing 2010年第1期7卷 31-38页

作者： Qi-Cong Wang Yuan-Hao Gong Chen-Hui Yang Cui-Hua Li Department of Computer Science, Xiamen University, Xiamen 361005, PRC Department of Computer Science Xiamen University Xiamen PRC

We propose a robust visual tracking framework based on particle filter to deal with the object appearance changes due to varying illumination, pose variantions, and occlusions. We mainly improve the observation model and re-sampling process in a particle filter. We use on-line updating appearance model, affine transformation, and M-estimation to construct an adaptive observation model. On-line updating appearance model can adapt to the changes of illumination partially. Affine transformation-based similarity measurement is introduced to tackle pose variantions, and M-estimation is used to handle the occluded object in computing observation likelihood. To take advantage of the most recent observation and produce a suboptimal Gaussian proposal distribution, we incorporate Kalman filter into a particle filter to enhance the performance of the resampling process. To estimate the posterior probability density properly with lower computational complexity, we only employ a single Kalman filter to propagate Gaussian distribution. Experimental results have demonstrated the effectiveness and robustness of the proposed algorithm by tracking visual objects in the recorded video sequences.

关键词： Visual tracking particle filter observation model Kalman filter expectation-maximization (EM) algorithm

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维普期刊数据库

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Estimation of Geodetic Parameters with VLBI Data of Last 5 Years

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Geo-Spatial Information Science 2007年第1期10卷 12-16页

作者： WEI Erhu LIU Jingnan SHI Chuang School of Geodesy and Geomatics Wuhan University 129 Luoyu Road Wuhan 430079 China.

The meaning to research the potential of VLBI for geodetic applications is summarized. And the observation models and their related parameters of geodetic interest are investigated. Then, the principle and method of using the random model in VLBI data processing are investigated. With the world wide VLBI data from 2000-2004, the conditions to compute the parameters of geodetic interest are introduced, and so are the computing methods and processes. And the computed resuits of the parameters of geodetic interest are analyzed.

关键词： VLBI observation model parameters of geodetic interest random model computation and analysis

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