  Responding to the challenge of detecting unusual radar targets in a well
identified environment, innovative anomaly and novelty detection methods keep
emerging in the literature. This work aims at presenting a benchmark gathering
common and recently introduced unsupervised anomaly detection (AD) methods, the
results being generated using high-resolution range profiles. A semi-supervised
AD (SAD) is considered to demonstrate the added value of having a few labeled
anomalies to improve performances. Experiments were conducted with and without
pollution of the training set with anomalous samples in order to be as close as
possible to real operational contexts. The common AD methods composing our
baseline will be One-Class Support Vector Machines (OC-SVM), Isolation Forest
(IF), Local Outlier Factor (LOF) and a Convolutional Autoencoder (CAE). The
more innovative AD methods put forward by this work are Deep Support Vector
Data Description (Deep SVDD) and Random Projection Depth (RPD), belonging
respectively to deep and shallow AD. The semi-supervised adaptation of Deep
SVDD constitutes our SAD method. HRRP data was generated by a coastal
surveillance radar, our results thus suggest that AD can contribute to enhance
maritime and coastal situation awareness.
