Motion Model Parameters Estimation for Electronic Image Stabilization Instrument

2013 ◽  
Vol 325-326 ◽  
pp. 1543-1546
Author(s):  
Xun Yu Zhong ◽  
Tian Hui Ren
Keyword(s):  
Motion Estimation ◽  
Least Squares Method ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Block Matching ◽  
Parameters Estimation ◽  
Transformation Model ◽  
Image Motion ◽  
Model Parameters ◽  
Image Stabilization

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

Model Parameters Estimation for Image Motion Compensation

2013 ◽  
Vol 347-350 ◽  
pp. 3672-3676
Author(s):  
Xun Yu Zhong ◽  
Xiao Shan Li ◽  
Yong Gang Zhao
Keyword(s):  
Motion Estimation ◽  
Least Squares Method ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Block Matching ◽  
Parameters Estimation ◽  
Transformation Model ◽  
Image Motion ◽  
Model Parameters ◽  
Macro Block

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

Real-Time Self-Tuning Speed Controller: Performance Comparison between Engine Fuelled with Palm Methyl Esters and Petroleum Diesel

2015 ◽  
Vol 815 ◽  
pp. 408-412
Author(s):  
M.N. Azuwir ◽  
Mohd Sazli Saad ◽  
Mohd Zakimi Zakaria
Keyword(s):  
Real Time ◽  
Least Squares Method ◽  
Methyl Esters ◽  
Performance Comparison ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Automotive Engine ◽  
Speed Controller ◽  
Self Tuning

This paper investigates the performance of a real-time self-tuning speed controller designed to track and regulate at various engine speeds. The controller was tested with an automotive engine fuelled with petroleum diesel and and palm oil biodiesel (Palm Methyl Esters) within speed range of 1800 rpm to 2400 rpm. A self-tuning control algorithm based on pole assignment method together with on-line model parameters estimation strategy based on the recursive least squares method are adopted. The ability of the controller to track, regulate at various engine speed and also to reject disturbances applied for both type of fuel are compared and presented. The results confirmed that the controller performed very satisfactorily.

A Space Time Autoaggressive Method Based on Parameters Estimation for Airborne MIMO Radar

2014 ◽  
Vol 556-562 ◽  
pp. 4496-4500
Author(s):  
Xing Hui Chen ◽  
Shi Qiao Gao
Keyword(s):  
Estimation Method ◽  
Mimo Radar ◽  
Space Time ◽  
Parameters Estimation ◽  
Ar Model ◽  
Model Parameters ◽  
Clutter Suppression ◽  
Training Samples ◽  
Multiple Input ◽  
Space Time Adaptive Processing

The clutter distribution of an airborne multiple input and multiple output (MIMO) radar in non-homogeneous environment varies with ranges and samples in different range gates are not independent identically distributed vectors, so that the statistical space time adaptive processing (STAP) methods degrade heavily. A clutter suppression method for airborne MIMO radar in non-homogeneous environments is studied in this paper. Firstly, Space time autoaggressive (STAR) method is introduced to airborne MIMO radar for clutter suppression and then an AR model parameters estimation method for STAR is proposed to decrease the complexity of traditional method. Simulation results show the proposed method can estimate parameters exactly and rapidly with only few training samples and be fit for clutter suppression in non-homogeneous environments.

scholarly journals A Rain-Rate Retrieval Algorithm for Attenuated Radar Measurements

2010 ◽  
Vol 49 (3) ◽  
pp. 381-393 ◽  
Author(s):  
Prabhat K. Koner ◽  
Alessandro Battaglia ◽  
Clemens Simmer
Keyword(s):  
Rain Rate ◽  
Least Squares Method ◽  
A Priori ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Retrieval Algorithm ◽  
W Band ◽  
Regularization Techniques ◽  
Radar Measurements ◽  
Regularized Total Least Squares

Abstract A dynamic regularization scheme for rain-rate retrievals from attenuated radar measurements is presented. Most regularization techniques, including the optimal estimation method, use the state-space parameters to regularize the problem, which will always lead to a bias in the solution. To avoid this problem the authors introduce an evolutionary regularization technique, which is based on the spatial derivative of the measured reflectivity profile and allows for a bias-free global solution. The regularization strength is determined by the quadratic eigenvalue solution using the regularized total least squares method. With the new method, the authors perform a retrieval of rain-rate profiles from simulated measurements of a nadir-pointing W-band (94 GHz) radar, in a configuration similar to the cloud radar employed on CloudSat. The simulations assume that multiple scattering is negligible and only liquid hydrometeors are taken into account. The authors compare the results of this method with the outcome of an optimal estimation method and demonstrate that their method is superior in terms of reliability, correlation coefficient, and dispersion to the optimal estimation method for layers experiencing high values of attenuation; therefore, the a priori bias typical for optimal estimation solutions is avoided.

Proportional Odds Model Affiliate with Random-Effect Longitudinal Model

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Chin Wan Yoke ◽  
Zarina Mohd Khalid
Keyword(s):  
Longitudinal Analysis ◽  
Proportional Hazards ◽  
Random Effect ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Joint Model ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Proportional Odds ◽  
Longitudinal Measurements

Joint survival-longitudinal analysis gains popularity in recent clinical studies. A proportional hazards (PH) model in survival sub-model is commonly an alternative path to simplify a complex covariates hazard model into a regression model. The PH model however closed only to the Weibull distribution, brought about inappropriate application for the log-logistic observations. Proportional odds (PO) model in that case raised forward to perform similarly with the PH model. The subsequent modelling study is therefore producing a joint PO-longitudinal analysis rather than a widely applicable joint PH-longitudinal analysis. Latent parameters is introduced as a linkage technique between the two sub-models. Investigation in this study relies on the simulation statistics in which the survival time-to-event data and longitudinal measurements are both influenced by a covariate effects. The repeatedly measures data additionally allow for different kind of missingness mechanisms. Maximum likelihood estimation method is applied to the joint model parameters estimation. The performance of the joint model and separated sub-models are then be compared. The illustrated results contributed better estimators on the joint model instead of separated model.

A Fast Block-Matching Motion Estimation Method Based on Difference Search Algorithm

2014 ◽  
Vol 610 ◽  
pp. 686-694
Author(s):  
Chang Jiang Liu ◽  
Chao Chen ◽  
A Fei Zhang ◽  
Xiao Lang Yan
Keyword(s):  
Motion Estimation ◽  
Search Algorithm ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Block Matching ◽  
Signal To Noise ◽  
Estimation Algorithms ◽  
Diamond Search ◽  
Simulation Results ◽  
Noise Ratio

The diamond search (DS) algorithm is one of the most efficient block matching motion estimation algorithms by far and has already been applied in MPEG2/4. Through our research, we found that there is still some redundancy in the algorithm. In this paper, an improved new difference based search (DBS) algorithm is proposed. Simulation results demonstrate that the new algorithm outperforms the well-known diamond search (DS) algorithm and four step-searches (4SS). It obtains almost the same Peak Signal to Noise Ratio (PSNR) while requires less computations than the DS algorithm and 4SS algorithm.

scholarly journals Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

2021 ◽  
Author(s):  
Mengtian Lu ◽  
Sicheng Lu ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Zhaokai Yin ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Assimilation Efficiency ◽  
Estimation Method ◽  
Initial Distribution ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Multiple Parameters ◽  
Basin Surface

Abstract Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).

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