A Reinforcement Learning-based Intelligent Learning Method for Anti-active Jamming in Frequency Agility Radar

Simple item page
dc.contributor.authorWei, J.
dc.contributor.authorYu, L.
dc.contributor.authorWei, Y.
dc.contributor.authorXu, R.
dc.coverage.issue4cs
dc.coverage.volume33cs
dc.date.accessioned2025-04-04T12:26:47Z
dc.date.available2025-04-04T12:26:47Z
dc.date.issued2024-12cs
dc.description.abstractActive jamming's flexibility and variability pose significant challenges for frequency-agility radar (FAR) detection, as it can continuously intercept and retransmit radar signals to suppress or deceive the radar. To tackle this, we propose an intelligent learning method for FAR based on reinforcement learning (RL), integrating signal processing with compressed sensing (CS). We introduce an inter-pulse carrier-frequency hopping combined with intra-pulse sub-frequency coding (IPCFH-IPSFC) signal model to address time-domain discontinuities caused by active jamming, enabling effective mutual masking of pulses through agile waveform parameters. We develop jamming signal models and design four jamming strategies based on two common types of active jamming, providing essential data for the FAR intelligent learning method. To enhance FAR’s adaptive anti-jamming and target detection performance, we propose an RL-based intelligent learning model. This model includes five submodules: signal processing, anti-jamming evaluation, target detection, optimization constraint design, and optimization algorithm design. We apply a proximal policy optimization combined with a generative pre-trained transformer (PPO-GPT) to solve this model, allowing FAR to adaptively learn jamming strategies and optimize IPCFH-IPSFC waveform parameters for effective anti-jamming. Simulation results confirm that our method achieves robust performance and rapid convergence, finding optimal anti-jamming strategies in just 215 training iterations. The FAR effectively counteracts jamming while accurately estimating target range and velocity.en
dc.formattextcs
dc.format.extent681-703cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2024 vol. 33, iss. 4, s. 681-703. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2024.0681en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/250817
dc.language.isoencs
dc.publisherRadioengineering societycs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2024/24_04_0681_0703.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectIntelligent learningen
dc.subjectanti-active jammingen
dc.subjectfrequency agility radaren
dc.subjectreinforcement learningen
dc.subjectcompressed sensingen
dc.titleA Reinforcement Learning-based Intelligent Learning Method for Anti-active Jamming in Frequency Agility Radaren
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta elektrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
24_04_0681_0703.pdf
Size:
3.18 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
2024/4