Optimal Time-Frequency Distribution Selection for LPI Radar Pulse Classification
In: 2020 IEEE International Radar Conference (RADAR), 2020-04-01
Online
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Zugriff:
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.
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Optimal Time-Frequency Distribution Selection for LPI Radar Pulse Classification
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Autor/in / Beteiligte Person: | Ritchie, Matthew ; Griffiths, Hugh ; Willetts, Ben |
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Zeitschrift: | 2020 IEEE International Radar Conference (RADAR), 2020-04-01 |
Veröffentlichung: | IEEE, 2020 |
Medientyp: | unknown |
DOI: | 10.1109/radar42522.2020.9114598 |
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