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CFARnet: Deep learning for target detection with constant false alarm rate
Tzvi Diskin
*
, Yiftach Beer
, Uri Okun
,
Ami Wiesel
*
Corresponding author for this work
The Rachel and Selim Benin School of Engineering and Computer Science
Research output
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Article
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peer-review
24
Scopus citations
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Keyphrases
Target Detection
100%
Deep Learning
100%
Constant False Alarm Rate
100%
Rate-constrained
33%
Machine Learning Techniques
16%
Computationally Expensive
16%
Generalized Likelihood Ratio Test
16%
Bayes Optimal
16%
Asymptotically Equivalent
16%
Deep Learning Framework
16%
Composite Test
16%
Optimal Detector
16%
Constrained Bayes
16%
Fitting Neural Network
16%
Computer Science
False Alarm Rate
100%
Deep Learning Method
100%
Neural Network
16%
generalized likelihood-ratio test
16%
Optimal Detector
16%
Machine Learning
16%
Learning System
16%
Deep Learning Framework
16%