关键词: 深空探测, 自组织网络, 智能感知, 智能组网, 智能管控

Abstract: With deep-space exploration evolving from single-probe missions toward multi-node coordination and from preprogrammed instructions to autonomous intelligence, traditional omnidirectional communication and fixed routing paradigms struggle to cope with harsh deep-space characteristics including ultra-long propagation delay, large-scale Doppler shift, celestial occultation and drastically time-varying topologies, imposing prominent constraints on link gain optimization, neighbor discovery, resource scheduling and security protection. Benefiting from superior performance in signal-to-noise ratio improvement, network capacity expansion, power consumption reduction and anti-interception capability enhancement, directional antennas combined with intelligent algorithms have emerged as a vital research solution to tackle complicated deep-space networking challenges. With deep-space exploration evolving from single-probe missions toward multi-node coordination and from preprogrammed instructions to autonomous intelligence, traditional omnidirectional communication and fixed routing paradigms struggle to cope with harsh deep-space characteristics including ultra-long propagation delay, large-scale Doppler shift, celestial occultation and drastically time-varying topologies, imposing prominent constraints on link gain optimization, neighbor discovery, resource scheduling and security protection. Benefiting from superior performance in signal-to-noise ratio improvement, network capacity expansion, power consumption reduction and anti-interception capability enhancement, directional antennas combined with intelligent algorithms have emerged as a vital research solution to tackle complicated deep-space networking challenges.This paper presents a comprehensive survey on intelligent directional ad hoc networking for deep-space exploration and systematically reviews key technologies and recent advances. It first analyzes the propagation features of directional deep-space links, antenna models and their performance gaps against omnidirectional counterparts, together with fundamental intelligent algorithm frameworks. Next, it elaborates on the deployment of intelligent algorithms for temporal-spatial synchronization, neighbor discovery and intelligent access in directional networks. On this basis, the work summarizes technologies covering highly dynamic topology maintenance, intelligent routing decision-making and multi-dimensional joint resource scheduling, and analyzes how the integration of artificial intelligence and network management improves network robustness and transmission efficiency. From the perspective of deep-space directional networking security, recent progress in authentication and AI-enabled network defense is reviewed to strengthen network survivability. Finally, grounded on China’s ongoing deep-space programs and intelligent networking developments, the paper concludes existing technical limitations and outlines prospective research directions.

Key words: deep space exploration, self-organizing network, intelligent perception, intelligent networking, intelligent management and control