Study on intelligent learning methods for multi-source autonomous navigation
The intelligent multi-source autonomous navigation system(IMANS),based on Beidou navigation and supported by inertial navigation,aims to deliver advanced PNT(positioning,navigation,and timing)services.It offers plug-and-play functionality,seamless sensor integration,imperceptible switching,and dependable performance to meet the challenges of complex and dynamic environments.By leveraging intelligent learning techniques,such as deep learning and reinforcement learning,and applying multi-scale analysis and complexity theory,the optimization and collaboration theories in IMANS are explored.This paper then develops intelligent learning frameworks prioritizing precision,stability,reliability,safety,generalizability,interpretability,and embedded system dynamics.These innovations contribute to the creation of resilient,robust,dynamic,controllable,and dependable precise IMANS models and methodology,driving the advancement of the national comprehensive PNT system and enabling the large-scale adoption of advanced navigation technologies.
intelligent multi-source autonomous navigationembedded system dynamicsgeneralizabilityinterpretabilitydependabilitycompleteness
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