IEICE TRANSACTIONS on Communications
Detection Performance Analysis of Distributed-Processing Multistatic Radar System with Different Multivariate Dependence Models in Local Decisions
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E105-B No.9 pp.1097-1104
- Publication Date
- 2022/09/01
- Publicized
- 2022/03/24
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBP3184
- Type of Manuscript
- PAPER
- Category
- Sensing
Authors
Van Hung PHAM
Le Quy Don Technical University
Tuan Hung NGUYEN
Le Quy Don Technical University
Hisashi MORISHITA
National Defense Academy
Keyword
Latest Issue
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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
Copy
@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},}
Copy
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 -
English
