SURFACE TRAFFIC AUTOMATION USING NEURAL NETWORKS

THIS PROJECT WILL IMPLEMENT A NEW APPROACH TO IDENTIFYING AIRCRAFT DURING GROUND OPERATIONS. WE WILL APPLY THE USE OFNEURAL NETWORK TECHNOLOGY FOR SCHEDULING AND TRACKING AIRCRAFT. THIS ROBUST AND MASSIVELY PARALLEL AUTOMATION TECHNIQUE HAS POTENTIAL APPLICATION FOR GROUND TRAFFIC MANAGEMENT. OUR CONCEPT SHOULD WORK WITH RUNWAY FOOTPRINT DETECTION SCHEMES. IT HAS THE POTENTIAL TO DO PATTERN RECOGNITION WHEN INTERFACED WITH ASDE-3 EQUIPMENT THAT WILL PROVIDE A BETTER IMAGE FOR CONTROLLER SITUATION VIEWING. THIS TECHNIQUE CAN BE IMPLEMENTED BASED UPON THE SUPERVISED LEARNING CONCEPTS AND POTENTIALLY IN SILICON AND COMPUTERS. OUR TECHNIQUE WILL ALSO USE HIERARCHICAL SCENE STRUCTURES THAT PERMIT BETTER IDENTIFICATION OF TARGETS VIEWED FROM DIFFERENT ANGLES. ADDITIONALLY, THIS TECHNIQUE WILL IMPROVE THE SPEED AND ACCURACY OF TARGET IDENTIFICATIONS FROM VIEWS TAKEN WHILE THE TARGET AND/OR RADAR IS IN MOTION.