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

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