Learning to Detect with Constant False Alarm Rate

Tzvi Diskin, Uri Okun, Ami Wiesel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageAmerican English
Title of host publication2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781665494557
DOIs
StatePublished - 2022
Event23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022 - Oulu, Finland
Duration: 4 Jul 20226 Jul 2022

Publication series

NameIEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
Volume2022-July

Conference

Conference23rd IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
Country/TerritoryFinland
CityOulu
Period4/07/226/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • deep learning
  • hypothesis testing

Fingerprint

Dive into the research topics of 'Learning to Detect with Constant False Alarm Rate'. Together they form a unique fingerprint.

Cite this