  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.
