An efficient neuromorphic approach for collision avoidance combining Stack-CNN with event cameras
This addresses space debris detection for space situational awareness, but appears incremental as it adapts an existing algorithm to a new sensor type.
The paper tackles the problem of detecting faint moving objects for space debris collision avoidance by using event-based cameras with a Stack-CNN algorithm, demonstrating enhanced signal-to-noise ratio in terrestrial testing.
Space debris poses a significant threat, driving research into active and passive mitigation strategies. This work presents an innovative collision avoidance system utilizing event-based cameras - a novel imaging technology well-suited for Space Situational Awareness (SSA) and Space Traffic Management (STM). The system, employing a Stack-CNN algorithm (previously used for meteor detection), analyzes real-time event-based camera data to detect faint moving objects. Testing on terrestrial data demonstrates the algorithm's ability to enhance signal-to-noise ratio, offering a promising approach for on-board space imaging and improving STM/SSA operations.