Near out-of-distribution detection for low-resolution radar micro-Doppler signatures
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 12 May 2022.
Builds on
17 of 42 references.
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5 more
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Citing papers per year: 2023: 3.
3 papers.