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.

本研究介绍了一种基于深度学习和传统方法的方案，用于实现雷达目标检测中的区分类似数据点且不需要监督的方法。通过对微多普勒信号的模拟研究，本文介绍了用于鉴别数据点所需要的特定监督要求，并讨论了训练中标签异常对训练集污染的影响。