论文标题
学习以恒定的错误警报率检测
Learning to Detect with Constant False Alarm Rate
论文作者
论文摘要
我们考虑将机器学习用于假设检验，重点是目标检测。基于古典模型的解决方案依赖于比较似然。这些对不完美的模型敏感，通常在计算上昂贵。相反，数据驱动的机器学习通常更强大，并且具有固定计算复杂性的分类器。学习的探测器通常具有较低复杂性的高精度，但在许多应用中所需的不持续的错误警报率（CFAR）。为了缩小这一差距，我们建议在任何零假设方案下促进检测器的相似分布的损失函数添加术语。实验表明，我们的方法以与竞争对手相似的准确性导致接近CFAR检测器。
We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical model-based solutions rely on comparing likelihoods. These 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 provide high accuracy with low complexity but do not have a constant false alarm rate (CFAR) as required in many applications. To close this gap, we propose to add a term to the loss function that promotes similar distributions of the detector under any null hypothesis scenario. Experiments show that our approach leads to near CFAR detectors with similar accuracy as their competitors.
