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2023 | OriginalPaper | Buchkapitel

Near Out-of-Distribution Detection for Low-Resolution Radar Micro-doppler Signatures

verfasst von : Martin Bauw, Santiago Velasco-Forero, Jesus Angulo, Claude Adnet, Olivier Airiau

Erschienen in: Machine Learning and Knowledge Discovery in Databases

Verlag: Springer Nature Switzerland

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Abstract

Near out-of-distribution detection (OODD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OODD use case for radar targets detection extensible to other kinds of sensors and detection scenarios. We emphasize the relevance of OODD and its specific supervision requirements for the detection of a multimodal, diverse targets class among other similar radar targets and clutter in real-life critical systems. We propose a comparison of deep and non-deep OODD methods on simulated low-resolution pulse radar micro-doppler signatures, considering both a spectral and a covariance matrix input representation. The covariance representation aims at estimating whether dedicated second-order processing is appropriate to discriminate signatures. The potential contributions of labeled anomalies in training, self-supervised learning, contrastive learning insights and innovative training losses are discussed, and the impact of training set contamination caused by mislabelling is investigated.

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Titel: Near Out-of-Distribution Detection for Low-Resolution Radar Micro-doppler Signatures
verfasst von: Martin Bauw
Santiago Velasco-Forero
Jesus Angulo
Claude Adnet
Olivier Airiau
Verlag: Springer Nature Switzerland
Buch: Machine Learning and Knowledge Discovery in Databases
Print ISBN: 978-3-031-26411-5

Electronic ISBN: 978-3-031-26412-2

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-26412-2_24

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