The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이전 연구에서 우리는 분산 처리 다중 정적 레이더 시스템의 탐지 성능을 평가하기 위해 코퓰러 이론을 기반으로 하는 새로운 방법을 제안했습니다. 여기서 로컬 결정의 종속성은 꼬리 종속성이 없고 선형 종속성이 있는 가우스 코퓰러로 모델링되었습니다. 그러나 우리는 또한 이 연구의 한 가지 주요 한계는 밀도가 긴 꼬리를 갖고 고해상도 레이더에서 클러터 및 원하는 신호를 모델링하는 데 자주 사용되는 로컬 탐지기 입력 간의 꼬리 의존성과 비선형 의존성에 대한 조사가 부족하다는 점을 지적했습니다. . 본 연구에서는 제안된 방법을 여러 유형의 다변량 코퓰러 기반 종속성 모델로 확장하여 꼬리 종속성과 다양한 종속성 모델이 시스템 탐지 성능에 미치는 영향을 자세히 설명함으로써 이러한 단점을 극복하려고 합니다. 우리의 신중한 분석은 두 가지 흥미롭고 중요한 설명을 제공합니다. 첫째, 꼬리 의존성에 따라 탐지 성능이 크게 저하됩니다. 둘째, 이러한 저하는 주로 위쪽 꼬리 종속성에서 비롯되는 반면 아래쪽 꼬리와 비선형 종속성은 예기치 않게 시스템 성능을 향상시킵니다.
Van Hung PHAM
Le Quy Don Technical University
Tuan Hung NGUYEN
Le Quy Don Technical University
Hisashi MORISHITA
National Defense Academy
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부
Van Hung PHAM, Tuan Hung NGUYEN, Hisashi MORISHITA, "Detection Performance Analysis of Distributed-Processing Multistatic Radar System with Different Multivariate Dependence Models in Local Decisions" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 9, pp. 1097-1104, September 2022, doi: 10.1587/transcom.2021EBP3184.
Abstract: In a previous study, we proposed a new method based on copula theory to evaluate the detection performance of distributed-processing multistatic radar systems, in which the dependence of local decisions was modeled by a Gaussian copula with linear dependence and no tail dependence. However, we also noted that one main limitation of the study was the lack of investigations on the tail-dependence and nonlinear dependence among local detectors' inputs whose densities have long tails and are often used to model clutter and wanted signals in high-resolution radars. In this work, we attempt to overcome this shortcoming by extending the application of the proposed method to several types of multivariate copula-based dependence models to clarify the effects of tail-dependence and different dependence models on the system detection performance in detail. Our careful analysis provides two interesting and important clarifications: first, the detection performance degrades significantly with tail dependence; and second, this degradation mainly originates from the upper tail dependence, while the lower tail and nonlinear dependence unexpectedly improve the system performance.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3184/_p
부
@ARTICLE{e105-b_9_1097,
author={Van Hung PHAM, Tuan Hung NGUYEN, Hisashi MORISHITA, },
journal={IEICE TRANSACTIONS on Communications},
title={Detection Performance Analysis of Distributed-Processing Multistatic Radar System with Different Multivariate Dependence Models in Local Decisions},
year={2022},
volume={E105-B},
number={9},
pages={1097-1104},
abstract={In a previous study, we proposed a new method based on copula theory to evaluate the detection performance of distributed-processing multistatic radar systems, in which the dependence of local decisions was modeled by a Gaussian copula with linear dependence and no tail dependence. However, we also noted that one main limitation of the study was the lack of investigations on the tail-dependence and nonlinear dependence among local detectors' inputs whose densities have long tails and are often used to model clutter and wanted signals in high-resolution radars. In this work, we attempt to overcome this shortcoming by extending the application of the proposed method to several types of multivariate copula-based dependence models to clarify the effects of tail-dependence and different dependence models on the system detection performance in detail. Our careful analysis provides two interesting and important clarifications: first, the detection performance degrades significantly with tail dependence; and second, this degradation mainly originates from the upper tail dependence, while the lower tail and nonlinear dependence unexpectedly improve the system performance.},
keywords={},
doi={10.1587/transcom.2021EBP3184},
ISSN={1745-1345},
month={September},}
부
TY - JOUR
TI - Detection Performance Analysis of Distributed-Processing Multistatic Radar System with Different Multivariate Dependence Models in Local Decisions
T2 - IEICE TRANSACTIONS on Communications
SP - 1097
EP - 1104
AU - Van Hung PHAM
AU - Tuan Hung NGUYEN
AU - Hisashi MORISHITA
PY - 2022
DO - 10.1587/transcom.2021EBP3184
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2022
AB - In a previous study, we proposed a new method based on copula theory to evaluate the detection performance of distributed-processing multistatic radar systems, in which the dependence of local decisions was modeled by a Gaussian copula with linear dependence and no tail dependence. However, we also noted that one main limitation of the study was the lack of investigations on the tail-dependence and nonlinear dependence among local detectors' inputs whose densities have long tails and are often used to model clutter and wanted signals in high-resolution radars. In this work, we attempt to overcome this shortcoming by extending the application of the proposed method to several types of multivariate copula-based dependence models to clarify the effects of tail-dependence and different dependence models on the system detection performance in detail. Our careful analysis provides two interesting and important clarifications: first, the detection performance degrades significantly with tail dependence; and second, this degradation mainly originates from the upper tail dependence, while the lower tail and nonlinear dependence unexpectedly improve the system performance.
ER -