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
Dans une étude précédente, nous avions proposé une nouvelle méthode basée sur la théorie des copules pour évaluer les performances de détection des systèmes radar multistatiques à traitement distribué, dans laquelle la dépendance des décisions locales était modélisée par une copule gaussienne avec dépendance linéaire et sans dépendance de queue. Cependant, nous avons également noté qu'une des principales limites de l'étude était le manque d'investigations sur la dépendance à la queue et la dépendance non linéaire entre les entrées des détecteurs locaux dont les densités ont de longues queues et sont souvent utilisées pour modéliser le fouillis et les signaux recherchés dans les radars à haute résolution. . Dans ce travail, nous tentons de surmonter cette lacune en étendant l'application de la méthode proposée à plusieurs types de modèles de dépendance multivariés basés sur des copules afin de clarifier en détail les effets de la dépendance de queue et des différents modèles de dépendance sur les performances de détection du système. Notre analyse minutieuse apporte deux précisions intéressantes et importantes : premièrement, les performances de détection se dégradent considérablement avec la dépendance à la queue ; et deuxièmement, cette dégradation provient principalement de la dépendance de la queue supérieure, tandis que la dépendance de la queue inférieure et la dépendance non linéaire améliorent de manière inattendue les performances du système.
Van Hung PHAM
Le Quy Don Technical University
Tuan Hung NGUYEN
Le Quy Don Technical University
Hisashi MORISHITA
National Defense Academy
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copier
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
Copier
@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},}
Copier
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 -