Koroi, Luke and Uluiburotu, Jiuliasi and Tiwari, Babita and Singh, Satyanand and Assaf, Mansour (2025) AI-Enhanced Radar Warning Receiver for Intercepted Pulse Parameter Estimation and Emitter Localization in Modern Aircraft Systems. In: Artificial Intelligence and Applications. Algorithms for Intelligent Systems . Springer Nature, Singapore. ISBN 978-981-95-0492-3
Full text not available from this repository.Abstract
Modern aircraft have Radar Warning Receivers (RWRs) to boost situational awareness and help counter threats through radar signal detection and evaluation. This study aims to simulate and analyze how RWR systems intercept L-band radar pulses from ground surveillance systems. The radar transmits chirp signals at 1.8 GHz carrier frequency with 30 MHz bandwidth through pulses that last 3 μs with 15 μs pulse repetition interval (PRI). The aircraft travels at a steady velocity of 200 m/s (around 0.6 Mach) while the radar station stays stationary at the origin. The RWR identifies transmitter location after analyzing radar signals by extracting waveform parameters, including pulse width and chirp bandwidth, along with carrier frequency. RWR applies signal processing methods to determine time of arrival (ToA), frequency of arrival (FoA), and Doppler shift due to aircraft velocity from the retrieved parameters. To determine the transmitter location relative to the aircraft system, utilize these criteria. The simulation showcases how RWRs can identify and process radar pulses under practical operating scenarios. The integration of sophisticated signal processing and localization techniques shows promising potential for enhancing decision-making and situational awareness in aerial platforms. Future development of AI-enhanced RWR systems can benefit from these results, which enhance threat detection, classification, and response abilities in modern aviation systems.
| Item Type: | Book Chapter |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Mansour Assaf |
| Date Deposited: | 08 Dec 2025 03:49 |
| Last Modified: | 08 Dec 2025 03:49 |
| URI: | https://repository.usp.ac.fj/id/eprint/15196 |
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