Parametric and Model Based Adaptive Detection Algorithms for Non-Gaussian Interference Backgrounds

Mapping Intimacies ◽

10.21236/ada369457 ◽

1999 ◽

Cited By ~ 1

Author(s):

Muralidhar Rangaswamy

Keyword(s):

Adaptive Detection ◽

Detection Algorithms ◽

Model Based ◽

Non Gaussian

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Model-based adaptive detection and DOA estimation using separated sub-arrays

Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322) ◽

10.1109/nrc.2002.999702 ◽

2003 ◽

Cited By ~ 4

Author(s):

C. Engdahl ◽

P. Sunnergren

Keyword(s):

Doa Estimation ◽

Adaptive Detection ◽

Model Based

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A Gaussian mixture regression model based adaptive filter for non-Gaussian noise without a priori statistic

Signal Processing ◽

10.1016/j.sigpro.2021.108314 ◽

2021 ◽

pp. 108314

Author(s):

Haoran Cui ◽

Xiaoxu Wang ◽

Shuaihe Gao ◽

Tiancheng Li

Keyword(s):

Regression Model ◽

Adaptive Filter ◽

Gaussian Noise ◽

A Priori ◽

Gaussian Mixture ◽

Mixture Regression ◽

Model Based ◽

Non Gaussian ◽

Mixture Regression Model

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Surrogate Model-Based Multi-timescale Stochastic Control of Islanded Cascaded Hydro-Solar System Considering Non-Gaussian Uncertainty

10.1109/icpsasia52756.2021.9621405 ◽

2021 ◽

Author(s):

Zhipeng Yu ◽

Yiwei Qiu ◽

Jin Lin ◽

Feng Liu ◽

Yonghua Song ◽

...

Keyword(s):

Solar System ◽

Stochastic Control ◽

Surrogate Model ◽

Model Based ◽

Non Gaussian

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Adaptive Detection Algorithms

Simplified Robust Adaptive Detection and Beamforming for Wireless Communications ◽

10.1002/9781118938218.ch3 ◽

2018 ◽

pp. 89-125

Keyword(s):

Adaptive Detection ◽

Detection Algorithms

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A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise

Proceedings of the 2016 SIAM International Conference on Data Mining ◽

10.1137/1.9781611974348.19 ◽

2016 ◽

Author(s):

Xi C. Chen ◽

Yuanshun Yao ◽

Sichao Shi ◽

Snigdhansu Chatterjee ◽

Vipin Kumar ◽

...

Keyword(s):

Change Point ◽

General Framework ◽

Change Point Detection ◽

Detection Algorithms ◽

Model Based ◽

Point Detection

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Design of adaptive detection algorithms for surveillance radar

Record of the IEEE 2000 International Radar Conference [Cat. No. 00CH37037] ◽

10.1109/radar.2000.851903 ◽

2002 ◽

Cited By ~ 16

Author(s):

W.L. Melvin ◽

J.R. Guerci ◽

M.J. Callahan ◽

M.C. Wicks

Keyword(s):

Adaptive Detection ◽

Detection Algorithms

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Performance characterization for Gaussian mixture model based motion detection algorithms

IEEE International Conference on Image Processing 2005 ◽

10.1109/icip.2005.1529946 ◽

2005 ◽

Cited By ~ 1

Author(s):

Junwen Wu ◽

M. Trivedi

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Motion Detection ◽

Gaussian Mixture ◽

Performance Characterization ◽

Detection Algorithms ◽

Model Based

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MODEL-BASED EVALUATION OK DIFFERENT QRS DETECTION ALGORITHMS FOR USE IN MOBILE SYSTEMS

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmte.2003.48.s1.210 ◽

2003 ◽

Vol 48 (s1) ◽

pp. 210-211

Author(s):

G. v. Wagner ◽

W. Storku ◽

K. Müller-Glaser

Keyword(s):

Mobile Systems ◽

Qrs Detection ◽

Detection Algorithms ◽

Model Based

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Non-Gaussian model-based fusion of noisy images in the wavelet domain

Computer Vision and Image Understanding ◽

10.1016/j.cviu.2009.09.002 ◽

2010 ◽

Vol 114 (1) ◽

pp. 54-65 ◽

Cited By ~ 52

Author(s):

Artur Loza ◽

David Bull ◽

Nishan Canagarajah ◽

Alin Achim

Keyword(s):

Gaussian Model ◽

Wavelet Domain ◽

Model Based ◽

Noisy Images ◽

Non Gaussian

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A New Spatial Correlation Model Based on the Distributed RC-∏ Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.2204 ◽

2014 ◽

Vol 989-994 ◽

pp. 2204-2207

Author(s):

Xiao Xiao Liu ◽

Jing Bo Shao ◽

Ling Ling Zhao

Keyword(s):

Gaussian Process ◽

Spatial Correlation ◽

Process Variations ◽

Deep Submicron ◽

Correlation Model ◽

Crosstalk Noise ◽

Model Based ◽

Reduction Techniques ◽

Non Gaussian ◽

Tree Branch

To solve the crosstalk noise question in deep-submicron technologies, a new spatial correlation model based on the distributed RC-π model is proposed in this paper. Quiet aggressor net and tree branch reduction techniques are introduced to the distributed RC-π model, and a new spatial correlation model of both Gaussian and non-Gaussian process variations among segments is created. Experimental results show that our method maintains the efficiency of past approaches, and significantly improves on their accuracy.

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