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

Tzvi Diskin, Yiftach Beer, Uri Okun, Ami Wiesel

Research output: Contribution to journalArticlepeer-review


We consider the problem of target detection with a constant false alarm rate (CFAR). This constraint is crucial in many practical applications and is a standard requirement in classical composite hypothesis testing. In settings where classical approaches are computationally expensive or where only data samples are given, machine learning methodologies are advantageous. CFAR is less understood in these settings. To close this gap, we introduce a framework of CFAR constrained detectors. Theoretically, we prove that a CFAR constrained Bayes optimal detector is asymptotically equivalent to the classical generalized likelihood ratio test (GLRT). Practically, we develop a deep learning framework for fitting neural networks that approximate it. Experiments of target detection in different settings demonstrate that the proposed CFARnet allows a flexible tradeoff between CFAR and accuracy.

Original languageAmerican English
Article number109543
JournalSignal Processing
StatePublished - Oct 2024


  • CFAR
  • Machine learning

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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