2022
DOI: 10.48550/arxiv.2208.02474
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

CFARnet: deep learning for target detection with constant false alarm rate

Abstract: We consider the problem of learning detectors with a Constant False Alarm Rate (CFAR). Classical model-based solutions to composite hypothesis testing are sensitive to imperfect models and are often computationally expensive. In contrast, data-driven machine learning is often more robust and yields classifiers with fixed computational complexity. Learned detectors usually do not have a CFAR as required in many applications. To close this gap, we introduce CFARnet where the loss function is penalized to promote… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 27 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?