DEEP FOCUS: USING DEEP LEARNING TO DISCERN TARGETS IN CLUTTERED RADAR

Deep Focus applies deep learning neural nets to Apache Fire Control Radar (FCR) targeting and target identification, with applicability to related systems. Recent innovations in deep learning theory and implementation have enabled neural nets to achieve what was once unthinkable: beat humans at complex image recognition skills, safely pilot cars over chaotic road systems, and overwhelm Grandmaster Lee Sedol in the game of Go, a challenge previously thought immune to AI because of the game's near-infinite complexity. Deep Focus analyzes the FCR return radar signal and accurately identifies targets despite extremely high noise. While training a deep neural net is computationally intensive and requires specialized hardware, execution is computationally inexpensive and can be implemented with very modest CPU and memory requirements. Phase 1 of the project determines feasibility by training a deep learning neural net to analyze radar images and output an accurate target identification. The success metrics include false negative and false positive rates for radar targets. Computer vision and deep learning are core CLOSTRA competences.