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Progressive Gaussian Filtering [article]

Uwe D. Hanebeck, Jannik Steinbring
2012 arXiv   pre-print
The performance of the new filter is evaluated in comparison with state-of-the-art filters by means of a canonical benchmark example, the discrete-time cubic sensor problem.  ...  The ODE is used for continuously tracking the true non-Gaussian posterior by its best matching Gaussian approximation.  ...  GPF), and the new progressive Gaussian filter.  ... 
arXiv:1204.0133v1 fatcat:vl5tmqbszvh6bnt5f2fzf6cizi

An Adaptive Fuzzy Neural Network Based On Progressive Gaussian Approximate Filter with Variable Step Size

Guorui Zhu
2022 Information Technology and Control  
on the progressive Gaussian approximate filter (PGAF), after that, PGAFVS can be deduced.  ...  As an advanced method that can effectively improve the robustness and accuracy of the system, the progressive Gaussian approximate filter with variable step size (PGAFVS) still has some shortcomings, how  ...  Gaussian approximate filter.  ... 
doi:10.5755/j01.itc.51.1.29776 fatcat:hgiiimjktvehllmsszcjq7smpa


2014 International Journal of Electronics Signals and Systems  
The proposed filter is able to efficiently suppress both Gaussian noise and impulse noise, as well as mixed Gaussian impulse noise.  ...  This paper is concerned with algebraic features based filtering technique, named as the adaptive statistical quality based filtering technique (ASQFT), is presented for removal of Impulse and Gaussian  ...  Based on the result of the estimation, an adaptive progressive filtering operation is employed in combination with optimized dimension and shape of processing windows computational efficiency of the ASQFT  ... 
doi:10.47893/ijess.2014.1162 fatcat:smpqovtm6vb2lgsjbuxk3cjeli


Arezki Younsi, M. Nadhor
2011 Progress in Electromagnetics Research B  
In the present paper, we deal with the performance analysis of the Adaptive Normalized Matched Filter (ANMF) detector in compound-Gaussian clutter with inverse gamma texture model and unknown covariance  ...  detection problem (1), is the Normalized Matched Filter (NMF) [13, 25, 26] .  ...  Radar, Adelaide, Australia, 2008 Progress In Electromagnetics Research B, Vol. 32, 2011  ... 
doi:10.2528/pierb11051905 fatcat:3rkfohecqnagrogdklazg37sby

A Novel Progressive Gaussian Approximate Filter with Variable Step Size Based on a Variational Bayesian Approach

Mingming Bai, Yulong Huang, Yonggang Zhang, Lyudmila Mihaylova, Jonathon Chambers
2019 ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Index Terms-Gaussian approximate filter, progressive measurement update, variable step size, variational Bayesian  ...  et al. (2 more authors) (2019) A novel progressive Gaussian approximate filter with variable step size based on a variational Bayesian approach.  ...  INTRODUCTION Gaussian approximate filters (GAFs) are the most common approach in various nonlinear applications since they can provide a better compromise between the computational cost and the estimation  ... 
doi:10.1109/icassp.2019.8682907 dblp:conf/icassp/BaiHZMC19 fatcat:qe64fjwd4jeglis6vud2dvrvpq

Efficient Nonlinear Measurement Updating based on Gaussian Mixture Approximation of Conditional Densities

Marco F. Huber, Dietrich Brunn, Uwe D. Hanebeck
2007 American Control Conference (ACC)  
The exponential growth of Gaussian mixture components resulting from repeated filtering is avoided implicitly by the prediction step using the proposed techniques.  ...  By treating the approximation task as an optimization problem, we use progressive processing to achieve high quality results.  ...  Since the likelihood is also represented by a Gaussian mixture, the filter step is reduced to simple multiplications of Gaussian densities resulting in a Gaussian mixture representation of the posterior  ... 
doi:10.1109/acc.2007.4282269 dblp:conf/acc/HuberBH07 fatcat:pnhrvxkwibgsjpnc3eojadzkbi

Reducing noise in suspected glaucomatous visual fields by using a new spatial filter

Stuart K Gardiner, David P Crabb, Fred W Fitzke, Roger A Hitchings
2004 Vision Research  
Here, we derive a physiologically accurate spatial filter to be applied to the data after patient examination. The filter was tested by a Virtual Eye computer simulation.  ...  By simulating series of stable fields it was shown that specificity of determining visual field changes was improved; while simulating progressing fields (based on a map of the optic nerve head) it was  ...  In this way, the sample probabilities of the points being flagged as progressing based on each of the Raw, Gaussian and Filtered data were calculated.  ... 
doi:10.1016/s0042-6989(03)00474-7 pmid:14967209 fatcat:jvj4mwarljcmdh4p5kni2qircy

Filter CLEAN — An Improved Method for CLEANing Images

Alan McPhail
2002 Symposium - International astronomical union  
Filter CLEAN is particularly good at recovering extended sources while maintaining good resolution on fine-scale sources. The Filter CLEAN algorithm is described and results presented.  ...  Filter CLEAN requires fewer iterations and the residual rms is much lower than results from Högbom CLEAN.  ...  This shorter spacing data is then separated into high-and low-resolution data with a Gaussian filter.  ... 
doi:10.1017/s0074180900169694 fatcat:disdt3vh2zezjpoqjt46lgu5ye

The Progressive Proposal Particle Filter: Better Approximations to the Optimal Importance Density [article]

Pete Bunch, Simon Godsill
2014 arXiv   pre-print
This works by introducing the observation progressively and performing a series of state updates, each using a local Gaussian approximation to the optimal importance density.  ...  For many highly nonlinear or non-Gaussian models, these approximations can be poor, leading to degeneracy of the particle approximation or even the filter "losing track" completely.  ...  With non-Gaussian model densities, the performance gains from the progressive proposal particle filter are more modest, when using either the scale mixture of normals method or Gaussian approximations  ... 
arXiv:1401.2791v2 fatcat:pwdmln2mbjhx3l3c3pl5mlfiqa

Closed-Form Prediction of Nonlinear Dynamic Systems by Means of Gaussian Mixture Approximation of the Transition Density

Marco Huber, Dietrich Brunn, Uwe Hanebeck
2006 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems  
Instead of directly approximating the predicted density, we propose the approximation of the transition density by means of Gaussian mixtures.  ...  We treat the approximation task as an optimization problem that is solved offline via progressive processing to bypass initialization problems and to achieve high quality approximations.  ...  Considering the filter step is also part of future work. Generally, the progressive processing offers room for improvement.  ... 
doi:10.1109/mfi.2006.265622 dblp:conf/mfi/HuberBH06 fatcat:txracac4vzbmtkcv5isb4cel7u

Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes

Fahmy F. F. Asal
2019 Advances in Remote Sensing  
This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter  ...  the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window  ...  curvature classification, progressive morphological filter and progressive triangulated irregular network.  ... 
doi:10.4236/ars.2019.82004 fatcat:2jua37sszzegnazdfblfjvlwdi

One-Dimensional Scale-Space Preserving Filters

R. Harvey, A. Bosson, J.A. Bangham
1998 Zenodo  
Examples of the simplification produced by the Gaussian filter and the m-sieve are shown in Figure 1 in which an example signal consisting of two pulses and an impulse is progressively simplified.  ...  In one dimension the unique solution to this problem, if one insists on a linear filter, is to filter with a Gaussian kernel.  ... 
doi:10.5281/zenodo.37035 fatcat:tr6zeey7wbcm5dyydaj4a3eov4

Ship Tracking Based on Underwater Electric Potential

Baoquan Sun, Bing Yan, Jiawei Zhang, Shouwei Hu
2018 Mathematical Problems in Engineering  
on progressive Bayesian; simulations are designed.  ...  Aiming at the problems existing in the traditional Kalman filters under large initial errors, a new nonlinear filter is proposed.  ...  A progressive Bayesian method without particles is derived under the Gaussian assumption in [15] .  ... 
doi:10.1155/2018/2797621 fatcat:nnf4j7wvc5bh3eg5azdn4lem3e

Classification of Initial Stages of Alzheimer's Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Ahsan Bin Tufail, Yong-Kui Ma, Mohammed K. A. Kaabar, Ateeq Ur Rehman, Rahim Khan, Omar Cheikhrouhou
2021 Mathematics  
We used box filtering, median filtering, Gaussian filtering, and modified Gaussian filtering approaches to preprocess the images and use them for classification using 3D-CNN architecture.  ...  architecture trained using modified Gaussian-filtered data performed the best.  ...  Here, modified Gaussian filtering means that Gaussian filtering is applied to the input volume, followed by a second round of Gaussian filtering.  ... 
doi:10.3390/math9233101 fatcat:hcmchz2wzfbmpk4rvbx6kcxe44

An Efficient Motion Adaptive De-interlacing and Its VLSI Architecture Design

Hongbin Sun, Nanning Zheng, Chenyang Ge, Dong Wang, Pengju Ren
2008 2008 IEEE Computer Society Annual Symposium on VLSI  
With a Gaussian filter, the motion detection can eliminate the influence of noise.  ...  This paper presents an efficient motion adaptive deinterlacing technique that consists of two main steps, i.e. 4-field extended Gaussian filtering motion detection and adjustable window ELA de-interlacing  ...  Therefore we utilize a 2d Gaussian filter to assist to detect the motions.  ... 
doi:10.1109/isvlsi.2008.46 dblp:conf/isvlsi/SunZGWR08 fatcat:hp53liw4ofgkfa6nn642hyqmhq
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