Copula based dependence modeling for inference in RADAR systems

Sora Choi; Hao He; Pramod Kumar Varshney

doi:10.1109/RadarConf.2015.7411879

Copula based dependence modeling for inference in RADAR systems

Sora Choi, Hao He, Pramod Kumar Varshney

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

Abstract

Statistical dependence is one of the significant design issues in various radar systems for inference tasks including detecting an activity of interest or estimating states or parameters for situational awareness. Modeling dependence has been discussed in many articles on radar and the research has shown that taking dependence into account improves performance of inference tasks. In this paper, we introduce copulas as flexible tools for modeling of nonlinear/linear dependence. Copulas allow one to model the dependence structures among random variables with arbitrary marginal distributions. We explore the potential use of copula theory in radar systems while discussing the dependence modeling problem. Then we present an application for binary hypothesis testing to show the benefit of using copula theory.

Original language	English (US)
Title of host publication	2015 IEEE Radar Conference - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	197-202
Number of pages	6
ISBN (Electronic)	9781467396554
DOIs	https://doi.org/10.1109/RadarConf.2015.7411879
State	Published - Feb 17 2015
Event	IEEE Radar Conference - Johannesburg, South Africa Duration: Oct 27 2015 → Oct 30 2015

Other

Other	IEEE Radar Conference
Country/Territory	South Africa
City	Johannesburg
Period	10/27/15 → 10/30/15

ASJC Scopus subject areas

Signal Processing
Instrumentation

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10.1109/RadarConf.2015.7411879

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title = "Copula based dependence modeling for inference in RADAR systems",

abstract = "Statistical dependence is one of the significant design issues in various radar systems for inference tasks including detecting an activity of interest or estimating states or parameters for situational awareness. Modeling dependence has been discussed in many articles on radar and the research has shown that taking dependence into account improves performance of inference tasks. In this paper, we introduce copulas as flexible tools for modeling of nonlinear/linear dependence. Copulas allow one to model the dependence structures among random variables with arbitrary marginal distributions. We explore the potential use of copula theory in radar systems while discussing the dependence modeling problem. Then we present an application for binary hypothesis testing to show the benefit of using copula theory.",

author = "Sora Choi and Hao He and Varshney, {Pramod Kumar}",

year = "2015",

month = feb,

day = "17",

doi = "10.1109/RadarConf.2015.7411879",

language = "English (US)",

pages = "197--202",

booktitle = "2015 IEEE Radar Conference - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "IEEE Radar Conference ; Conference date: 27-10-2015 Through 30-10-2015",

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AU - He, Hao

AU - Varshney, Pramod Kumar

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N2 - Statistical dependence is one of the significant design issues in various radar systems for inference tasks including detecting an activity of interest or estimating states or parameters for situational awareness. Modeling dependence has been discussed in many articles on radar and the research has shown that taking dependence into account improves performance of inference tasks. In this paper, we introduce copulas as flexible tools for modeling of nonlinear/linear dependence. Copulas allow one to model the dependence structures among random variables with arbitrary marginal distributions. We explore the potential use of copula theory in radar systems while discussing the dependence modeling problem. Then we present an application for binary hypothesis testing to show the benefit of using copula theory.

AB - Statistical dependence is one of the significant design issues in various radar systems for inference tasks including detecting an activity of interest or estimating states or parameters for situational awareness. Modeling dependence has been discussed in many articles on radar and the research has shown that taking dependence into account improves performance of inference tasks. In this paper, we introduce copulas as flexible tools for modeling of nonlinear/linear dependence. Copulas allow one to model the dependence structures among random variables with arbitrary marginal distributions. We explore the potential use of copula theory in radar systems while discussing the dependence modeling problem. Then we present an application for binary hypothesis testing to show the benefit of using copula theory.

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DO - 10.1109/RadarConf.2015.7411879

M3 - Conference contribution

AN - SCOPUS:84971238262

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BT - 2015 IEEE Radar Conference - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Radar Conference

Y2 - 27 October 2015 through 30 October 2015

ER -

Copula based dependence modeling for inference in RADAR systems

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