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Distributed target detection based on gradient test in deterministic subspace interference

delete2025-02-01
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PRE
AI
P
Peiqin Tang
Z
Zhenyu Xu
H
Hong Xu *
刘伟建 cover
刘伟建 (Weijian Liu)
刘君 (Jun Liu)
Y
Yinghui Quan
DOI:10.1016/j.sigpro.2024.109673delete
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Abstract

Abstract

En 中文
This paper investigates the problem of distributed target detection in the presence of interference and Gaussian noise, where the target signal and interference are assumed to lie in different deterministic subspaces. Building upon this assumption, we propose several adaptive detectors resorting to the gradient criterion tailored for homogeneous environment and partially homogeneous environment. Simulation results indicate that the proposed gradient-based detectors outperform their competitors in some scenarios. Furthermore, all of these Gradient-based detectors exhibit the constant false alarm rate (CFAR) property.
Keywords:
Adaptive detection
Distributed target
Subspace interference
Gradient test

Journal

Signal Processing cover
Signal Processing
IF:
3.6
Papers:
9.9K
Citations:
1.7W

Organization

A
air force early warning academy
Scholars:
359
Papers: 258
Citations: 0
X
Xidian University
Scholars:
2.4W
Papers: 1.9W
Citations: 9.7K
C
chinese academy of sciences
Scholars:
56.3W
Papers: 44.8W
Citations: 704
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