Artificial Intelligence Fusion of Information for Aerospace (AIFIA) Systems

Abstract

For intelligence and surveillance methods, information fusion techniques typically increase the accuracy and reliability of results through uncertainty reduction. Recently, artificial intelligence and machine learning (AI/ML) methods presented the engineering community with numerous opportunities to improve avionics controls, structural health monitoring, and platform design. Hence, alignment of traditional Fusion Integration of Sensor Harvesting (FISH) with that of AI/ML should advance aerospace performance. As such, Artificial Intelligence Fusion of Information for Aerospace (AIFIA) Systems relies on multiple sensors for systems and situation identification. This paper identifies some aerospace applications of AI enhancing information fusion methods; while outlining areas of future opportunity in aerospace systems design. The example utilizes the ESCAPE data using machine and deep learning (DL) to monitor an area, detect moving platforms, and meeting certifiable bounds.