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

Area of Science:

• Optics and Photonics
• Computer Vision
• Automotive Engineering

Background:

• Laser jamming poses a significant threat to automotive cameras, particularly with the rise of light detection and ranging (LiDAR) systems.
• LiDAR deployment increases risks of detector saturation and image distortion in camera systems.

Purpose of the Study:

• To propose and validate an anti-laser-jamming imaging strategy for automotive cameras.
• To ensure continuous and reliable image acquisition despite laser interference.

Main Methods:

• Developed an anti-laser-jamming imaging strategy based on correlated double sampling (CDS).
• Implemented an alternating dual-channel sampling mechanism within the camera system.
• Verified the strategy through laboratory simulations, mechanical LiDAR jamming, and real-vehicle road tests using a short-wave infrared (SWIR) camera.

Main Results:

• The CDS-based strategy effectively suppressed LiDAR-induced disturbances.
• Stable and high-fidelity imaging performance was achieved even under laser jamming conditions.
• The camera remained operational, acquiring complete image frames during jamming events.

Conclusions:

• The proposed CDS strategy offers a practical and scalable solution for enhancing automotive imaging sensor resilience against laser jamming.
• This technology has broad applications in autonomous driving, surveillance, and security systems.
• The method ensures the reliable operation of cameras in environments with potential laser interference.