论文标题
CFARNET：以恒定的错误警报率的深度学习目标检测
CFARnet: deep learning for target detection with constant false alarm rate
论文作者
论文摘要
我们考虑以恒定的错误警报率（CFAR）的目标检测问题。在许多实际应用中，此约束至关重要，并且在经典的综合假设检验中是标准要求。在经典方法在计算上昂贵或仅给出数据样本的设置中，机器学习方法是有利的。在这些环境中，CFAR的理解较少。为了缩小此差距，我们引入了CFAR约束检测器的框架。从理论上讲，我们证明了CFAR受约束的最佳检测器在渐近上等同于经典的广义似然比检验（GLRT）。实际上，我们开发了一个深度学习框架，用于拟合近似它的神经网络。在不同环境中进行目标检测的实验表明，提出的CFARNET可以在CFAR和准确性之间进行灵活的权衡。
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.
