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Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms

Gusland, D; Christiansen, JM; Torvik, B; Fioranelli, F; Gurbuz, SZ; Ritchie, M; (2021) Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms. In: 2021 IEEE Radar Conference (RadarConf21). IEEE: Atlanta, GA, USA. Green open access

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Abstract

In this paper, we discuss an "open radar initiative" aimed at promoting the sharing of radar datasets and a common framework for acquiring data. The framework is based on widely available and affordable short-range radar hardware (automotive FMCW radar transceivers). This framework and initiative are intended to create and promote access to a common shared dataset for the development and benchmarking of algorithms. While this is the norm for image processing and speech processing research, there has been reluctance in the radar community so far to create common datasets of shared data, often due to justified intellectual property or security classification reasons. Notable exceptions do exist, such as the MSTAR dataset of SAR images, which enabled great progress across the radar research community for a number of years. With this initiative, we hope to stimulate discussion and, with time, changes of practice in the radar research community. The main contribution of this work relative to previously shared datasets of radar data is that the proposed framework consists of a complete, integrated and replicable hardware and software pipeline, allowing users to not only download existing data, but also to acquire their own data with a compatible format that allows expansion and enrichment of the common dataset.

Type: Proceedings paper
Title: Open Radar Initiative: Large Scale Dataset for Benchmarking of micro-Doppler Recognition Algorithms
Event: 2021 IEEE Radar Conference (RadarConf21)
Location: IEEE Atlanta Sect, ELECTR NETWORK
Dates: 08 May 2021 - 14 May 2021
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/RadarConf2147009.2021.9455239
Publisher version: https://doi.org/10.1109/RadarConf2147009.2021.9455...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Pipelines, Software algorithms, Radar, Radar imaging, Benchmark testing, Hardware, Transceivers
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10134022
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