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.
|Title of host publication
|2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2022
|23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022 - Oulu, Finland
Duration: 4 Jul 2022 → 6 Jul 2022
|IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
|23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
|4/07/22 → 6/07/22
Bibliographical notePublisher Copyright:
© 2022 IEEE.
- deep learning
- hypothesis testing