Development of an Advanced Pilot Assistant System Based on Multiple Surveillance Sensor and Deep Learning for GA Class Aircraft Part II System Configuration and Flight Test

초록

In this paper, the design, fabrication, and flight test process of a multi-sensor and deep learning-based pilot assistance system applicable to manned/unmanned aerial vehicles is represented. Unlike the TCAS-II system, the developed pilot assistance system not only can exchange position information between co-operative aircraft through Automatic Dependent Surveillance-Broadcast (ADS-B) but also includes air-to-air radar, camera sensors as well as an artificial intelligence algorithm that can detect a non-co-operative intruder and issue advisory warning to help the pilot in avoiding air collision accidents. this paper is split into 2 parts, and in part II the system's requirements and design, equipment selection, avoidance algorithm and the system flight test process and results are explained.

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