2026-05-25T15:42:25Z https://api.figshare.com/v2/oai
oai:figshare.com:article/30752767 2025-12-02T10:29:29Z category_26524 category_29173 category_26503 portal_549 item_type_3 month_year_12_2025
10.5522/04/30752767.v1 https://figshare.com/articles/dataset/RadarML_Dataset/30752767 https://ndownloader.figshare.com/files/60005198 Ritchie, Matt Matt Ritchie 0000-0001-8423-8064 White, Ryan Ryan White Hosford, Adam Adam Hosford <![CDATA[RadarML Dataset]]> Radio frequency engineering Machine learning not elsewhere classified Electronic sensors ES Radar and communication system 2025-12-02 2025-12-02 Dataset 2025 University College London This is the "RadarML" dataset a publicly available dataset of experimentally captured modulated radar pulses recorded over-the-air across multiple independent receive channels.

The dataset was captured on the UCL ARESTOR platform which is a Radio-Frequency-System-on-a-chip (RFSoC). Each waveform was designed based on its central frequency, bandwidth, duration and modulation type. The overall dataset contains over 2 million waveforms across 7 different modulation types. The goal of this dataset is to enable comparative analysis of Radar Modulation Classification techniques on real data and stimulate research into multi-channel signal detection methods.

Baseline detection and classification results are presented in the linked publication, showing how the performance varies as a function of Signal to Noise ratio.

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