Related papers: Learning to Detect with Constant False Alarm Rate

Learning to Detect with Constant False Alarm Rate

URL: http://arxiv.org/abs/2206.05747v1
Date: Sun, 12 Jun 2022 14:32:40 GMT
Title: Learning to Detect with Constant False Alarm Rate
Authors: Tzvi Diskin, Uri Okun, Ami Wiesel
Abstract summary: We consider the use of machine learning for hypothesis testing with an emphasis on target detection. We propose to add a term to the loss function that promotes similar distributions of the detector under any null hypothesis scenario.
Score: 2.2559617939136505
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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