# From Unsupervised to Semi-supervised Anomaly Detection Methods for HRRP Targets

arXiv: https://arxiv.org/abs/2106.11168 · HTML: https://mlsanity.com/paper/2106.11168/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2021.

## Built on by (most cited first, 4 of 4)

- [Near out-of-distribution detection for low-resolution radar micro-Doppler signatures](https://mlsanity.com/paper/2205.07869/) (2022)
- [MFN Decomposition and Related Metrics for High-Resolution Range Profiles Generative Models](https://mlsanity.com/paper/2602.13296/) (2026)
- [Conditional Generative Models for High-Resolution Range Profiles: Capturing Geometry-Driven Trends in a Large-Scale Maritime Dataset](https://mlsanity.com/paper/2602.13297/) (2026)
- [High-Resolution Range Profile Classifiers Require Aspect-Angle Awareness](https://mlsanity.com/paper/2603.00087/) (2026)

## Abstract (arXiv)

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.