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

Arwin Datumaya Wahyudi Sumari1,2, Rosa Andrie Asmara3, Ika Noer Syamsiana1

  • 1Department of Electrical Engineering, State Polytechnic of Malang, Malang 65141, East Java, Indonesia.

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Summary

This study introduces a novel machine learning method to identify "black flights" by combining radar data with Radar Cross Section (RCS). This enhances national air defense by improving the detection of unidentified aircraft in sovereign airspace.

Keywords:
Air speedAltitudeArtificial intelligenceBlack flight identificationMachine Learning with Aircraft's RCS, Altitude, and Air SpeedMachine learningRadar cross sectionRecommender System

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Area of Science:

  • Aerospace Engineering
  • National Security
  • Machine Learning Applications

Background:

  • Identifying aircraft that disable identification systems (Identification Friend or Foe, Automatic Dependent Surveillance Broadcast) poses a significant challenge for air defense.
  • Unidentified aircraft, termed 'black flights,' can pose threats to national airspace sovereignty.
  • Traditional radar systems (Primary Surveillance Radar, Secondary Surveillance Radar) provide limited identification capabilities for these aircraft.

Purpose of the Study:

  • To develop an advanced method for identifying black flights.
  • To enhance national air defense capabilities against covert aerial intrusions.
  • To improve the situational awareness and decision-making processes for air operations commands.

Main Methods:

  • A novel machine learning approach combining aircraft speed, altitude, and Radar Cross Section (RCS) data for identification.
  • Development of an integrated information system merging military radar Plan Position Indicator (PPI) displays with Automatic Dependent Surveillance Broadcast (ADS-B) data.
  • Formulation of new national air defense procedures for handling unidentified aerial threats.

Main Results:

  • Successful development of a machine learning model for enhanced black flight identification.
  • Creation of an information system that accelerates decision-making by integrating diverse data streams.
  • Establishment of a refined framework for national air defense strategies.

Conclusions:

  • The proposed machine learning method offers a significant advancement in identifying aircraft that intentionally mask their identity.
  • The integrated information system improves the operational efficiency of air defense commands.
  • This research contributes a new paradigm for national air defense against sophisticated aerial threats.