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Segmented Multi-Subpulse Waveform Processing for Cognitive Radar System
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AI
DOI：10.1109/TCCN.2026.3657044.png)
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Abstract
En 中文
Cognitive radar (CR) can adaptively adjust both the transmitted waveform and the receive processing strategy in response to environmental conditions and target characteristics, making it a highly investigated technology in anti-jamming applications. To counter rapid-replication and retransmission jamming generated by digital radio frequency memory (DRFM) systems, this paper introduces a cognitive processing framework utilizing segmented multi-subpulse waveforms. The framework features a multi-subpulse waveform design that combines complete complementary codes (CCCs) with intra-pulse frequency agility, enhancing inter-subpulse isolation and effectively mitigating jamming leakage between jammed and jamming-free subpulses. By exploiting discrepancies in the range-velocity distributions of jamming-induced false targets across different subpulse combinations, an iterative false target suppression method using random segmented pulse compression and range-velocity matching (RSPC-RVM) is developed. The method dynamically selects different subpulse combinations in each iteration, adaptively improving the difference of real and false targets. Numerical simulations demonstrate effective suppression of false targets induced by both jamming and range sidelobes, yielding a high false target elimination rate under challenging jamming scenarios while exhibiting robustness to jamming parameter variations. Validation via a semi-physical experiment further confirms the practical feasibility of the approach.
Keywords:
Digital radio frequency memory (DRFM) jamming
multi-subpulse
random segmented pulse compression (RSPC)
range-velocity matching (RVM)
cognitive radar (CR)
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1.5K
Citations:
5.5K
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