Adaptive Barrage Jamming Against SAR Based on Prior Information and Scene Segmentation
Due to the advantages of easy implementation and fine jamming effect, barrage jamming against synthetic aperture radar (SAR) has received extensive attention in the field of electronic countermeasures. However, most methods of barrage jamming still have limitations, such as uncontrollable jamming position and coverage and high-power requirements. To address these issues, an improved barrage jamming method is proposed in this paper. The proposed method fully combines the prior information of the region of interest (ROI), and the precise jamming with controllable position, coverage, and power is realized. For the proposed method, the ROI is firstly divided into several sub-scenes according to the obtained prior information, and the signal is intercepted. Then the frequency response function of the jammer for each sub-scene is generated. The frequency response function of the jammer, which consists of position modulation function and jamming coverage function, is decomposed into slow-time-dependent parts and slow-time-independent parts. The slow-time-independent parts are generated offline in advance, and the real-time performance of the proposed method is guaranteed through this way. Finally, the intercepted signal is modulated by the frequency response function to generate the two-dimensional controllable jamming effect. Theoretical analysis and simulation results show that the proposed method can produce jamming effects with controllable position and coverage, and the utilization efficiency of jamming power is improved.
Authors
- Zewen Fu (ORCID: https://orcid.org/0009-0004-2936-1755)
- Zhengwei Guo
- Xuebo Zhang (ORCID: https://orcid.org/0000-0001-5164-754X)
- Zhenchang Liu (ORCID: https://orcid.org/0009-0007-1372-6045)
- Longyuan Wang (ORCID: https://orcid.org/0000-0003-2845-8171)
- Ning Li (ORCID: https://orcid.org/0000-0002-4358-6449)
Institutions
- Henan University (CN)
- Kaifeng University (CN)
- Northwest Normal University (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-04-05
- DOI
- https://doi.org/10.3390/rs17071303
- Citations
- 1
- Primary Topic
- Advanced SAR Imaging Techniques
- Type
- article
- Field-Weighted Citation Impact
- 4.99
Funders
- Natural Science Foundation of Henan Province