JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v10i2.14380ArticleOptimization Strategy for Radar Signal Anti- Jamming in UAV Emergency Deliveryhttps://artdesignp.com/journal/JERA/10/2/10.26689/jera.v10i2.14380YangFan20261022026-04-03Drones in emergency delivery operations face complex electromagnetic interference, including active jamming such as suppression and deception types, and passive jamming like chaff, which seriously threatens the reliability of radar detection. Constrained by platform payload and computational power, traditional anti-jamming techniques are difficult to apply directly. This study proposes a set of optimization strategies centered on lightweight design and adaptability, including interference-aware waveform optimization, joint spatiotemporal processing, jamming evasion methods, and lightweight algorithm design. Through theoretical analysis and numerical simulation, the strategy achieves a Signal-to-Interference-plus-Noise Ratio (SINR) improvement of up to 22 dB under compound interference, maintains a stable target detection probability above 90%, and reduces computational complexity to only 30–45% of that of traditional methods. This effectively balances performance with resource constraints, thereby enhancing the reliability of delivery missions.

Chen H, 2025, Application Analysis of UAV Radar Flow Measurement System in the Pearl River Estuary. Pearl River, 46(S2): 34–36.

Zhou H, Wang Z, Guo Z, 2022, A Review of Radar Active Jamming Recognition Algorithms. Journal of Data Acquisition and Processing, 37(1): 1–20.

Wang K, Zhang J, Zhou Q, 2021, STAP Forward Scatter Jamming Method Based on Clutter Spreading. Journal of Air Force Engineering University (Natural Science Edition), 22(1): 62–69.

Chen T, Qiu B, Xiao Y, et al., 2024, Radar Signal Sorting Method Based on Point Cloud Segmentation Network. Journal of Electronics & Information Technology, 46(4): 1391–1398.

Cui G, Yu X, Wei W, et al., 2022, Review and Prospect of Cognitive Intelligent Radar Anti-Jamming Technology. Journal of Radars, 11(6): 974–1002.

Zhang Y, Li K, Shao T, et al., 2025, Joint Waveform Design for Multi-Target Detection in Cognitive MIMO Radar. International Journal of Pattern Recognition and Artificial Intelligence, 39(11): 2550015-1–2550015-18.