Willetts, B;
Ritchie, M;
Griffiths, H;
(2020)
Optimal Time-Frequency Distribution Selection for LPI Radar Pulse Classification.
In:
Proceedings of the 2020 IEEE International Radar Conference (RADAR).
(pp. pp. 327-332).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
The work presented in this paper shows the performance of various time-frequency distributions when gathering ELectronic INTelligence (ELINT) from an electromagnetic environment that contains transmissions from radars operating in a Low Probability of Interception (LPI) mode. A radar device varying waveform parameters on a pulse-by-pulse basis to enhance sensing capabilities and/or to avoid interception warrants a method that can assign a unique Pulse Descriptor Word (PDW) to each pulse detected. The simulations presented here makes use of a Deep Learning classifier that is fed by time-frequency representations of noisy LFM and FMCW pulses that each have unique signal parameters. The performance of the radar pulse classifier is conveyed for multiple time-frequency methods. The results show that the time-frequency representation requirements for accurate PDW generation varies for each signal parameter being estimated whilst also having a dependence on the SNR of the intercepted signal.
Type: | Proceedings paper |
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Title: | Optimal Time-Frequency Distribution Selection for LPI Radar Pulse Classification |
Event: | 2020 IEEE International Radar Conference (RADAR) |
Location: | Washington (DC), USA |
Dates: | 28th-30th April 2020 |
ISBN-13: | 978-1-7281-6813-5 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/radar42522.2020.9114598 |
Publisher version: | https://doi.org/10.1109/RADAR42522.2020.9114598 |
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: | ELINT, Waveform Classification, Deep Learning, Time-frequency analysis, LPI waveforms |
UCL classification: | UCL UCL > Provost and Vice Provost Offices 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/10109342 |
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