Interference Environment Model Recognition for Robust Adaptive Detection

The recognition of the interference environment for adaptive radar detection is addressed in this article. Typically, detectors are designed in one specific scenario which may not be appropriate for the varying interference environment, especially for the airborne and space-based radar system. In th...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2020-08, Vol.56 (4), p.2850-2861
Main Authors: Shang, Zheran, Huo, Kai, Liu, Weijian, Wang, Yongliang, Li, Xiang
Format: Article
Language:English
Subjects:
Adaptation models
Adaptive detection
Airborne radar
Atmospheric modeling
Clutter
Criteria
Data models
Detectors
Environment models
Homogeneity
Interference
interference environment model recognition
Model accuracy
model order selection (MOS)
parameter estimation
partial homogeneity
Radar
Radar detection
Radar equipment
Random processes
Recognition
Robustness (mathematics)
Space based radar
spherically invariant random process (SIRP)
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Summary:The recognition of the interference environment for adaptive radar detection is addressed in this article. Typically, detectors are designed in one specific scenario which may not be appropriate for the varying interference environment, especially for the airborne and space-based radar system. In this article, the considered recognition task is cast in terms of multiple hypothesis tests and the theory of model order selection (MOS) techniques are exploited to devise suitable decision rules. The interference environments are divided into homogeneity, partial homogeneity, and spherically invariant random process. Three MOS techniques, namely, the Akaike information criterion (AIC), generalized information criterion, and corrected AIC, are adopted. At the analysis stage, illustrating examples for the influence of the environment model parameters on the recognition accuracy of the MOS rules are presented. Numerical experiments show the AIC rule has the most robust recognition performance.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2019.2954153