Anti-laser-jamming imaging strategy for cameras based on correlated double sampling technique

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Abstract
Laser jamming has emerged as a critical challenge for cameras, with the widespread deployment of automotive light detection and ranging (LiDAR), the risks of detector saturation and image distortion have increased significantly. To address this problem, this paper proposed an anti-laser-jamming imaging strategy based on correlated double sampling (CDS). Through an alternating dual-channel sampling mechanism, the camera can remain operational under laser jamming, ensuring the acquisition of at least one complete image frame. We constructed an experimental system consisting of a laser jamming unit, a photoelectric detection and control unit, a high-speed comparator unit, and a short-wave infrared (SWIR) camera to carry out the verification. Laboratory simulation experiments were conducted under different laser repetition frequencies and pulse widths, followed by anti-laser-jamming experiments using mechanical LiDAR systems and real-vehicle tests in road environments. The results demonstrate that the CDS-based anti-laser-jamming strategy effectively suppresses LiDAR-induced disturbances, achieving stable and high-fidelity imaging performance in road environments. This study provides a practical and scalable solution to enhance the evasion of automotive imaging sensors against laser jamming, offering broad application prospects in autonomous driving, surveillance, and security.
Funding Information
  • National Natural Science Foundation of China (62405163)
  • National Natural Science Foundation of China (52427901)
  • Shandong University (XYJG2023129)
  • Shandong University (yr20240105)
  • Fundamental Research Funds for the Central Universities (2021JCG018)
  • Natural Science Foundation of Shandong Province (ZR2022LLZ002)
  • Natural Science Foundation of Shandong Province (ZR2022MF323)
  • Natural Science Foundation of Shandong Province (ZR2024MF110)

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