A new method and information system based on artificial intelligence for black flight identification

Identification of aircraft entering a country's sovereign airspace if it shuts down its identification system, either the Identification Friend or Foe system and/or the Automatic Dependent Surveillance Broadcast system, has long been a challenge for the National Air Operations Command. Aircraft that do not want their identities to be revealed are called black flights and generally have certain missions that can interfere with the sovereignty of a country's airspace. Military radar units that have the task of monitoring airspace are generally equipped with Primary Surveillance Radar that detects the presence of aircraft in their operating area and Secondary Surveillance Radar which functions to identify the aircraft. In the case of black flight, data from the radar in the form of airspeed, altitude, and position are not able to help identify the identity of the black flight. The contributions of this research are:

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  a new method of black flight identification that combines air speed data and altitude with Radar Cross Section (RCS) data using machine learning,
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  a new information system that combines the display of the Plan Position Indicator (PPI) of military radar and ADS-B to accelerate decision-making on black flight,
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  a new approach to national air defense procedures.