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 setting demonstrate that the proposed CFARnet allows a flexible tradeoff between CFAR and accuracy.

翻译：我们考虑具有恒定虚警率（CFAR）的目标检测问题。这一约束在许多实际应用中至关重要，是经典复合假设检验的标准要求。在经典方法计算成本高昂或仅提供数据样本的场景下，机器学习方法具有明显优势。然而，在这些场景中CFAR的认知尚不充分。为弥补这一空白，我们提出了CFAR约束检测器框架。理论上，我们证明CFAR约束下的贝叶斯最优检测器渐近等价于经典广义似然比检验（GLRT）。实践层面，我们开发了用于拟合逼近该最优检测器的神经网络深度学习框架。不同场景下的目标检测实验表明，所提出的CFARnet能够实现CFAR与检测精度之间的灵活权衡。