An Intelligent Air Defense Recommendation Model based on Fuzzy Inference System

Securing the nation’s airspace demands the integration of an advanced air defense system. Automation technology can be incorporated to deploy anti-missile rockets, acting as a deterrent against various air-missile threats. This research aims to identify potential threats within a protected area and integrate an automatic system into the air defense measures based on detection. In the event of identifying incoming missiles, decisions will be made by gathering and validating information on specific issues through dedicated radars. Special emphasis is placed on utilizing a Fuzzy system to determine the nature of the threat, distinguishing between scrambling or surface-to-air missile launches based on distance, secondary radar input, and the speed of the airborne object. The paper provides a comprehensive comparison of defuzzification methods, demonstrating their effectiveness in modern air defense systems. The chosen method proves suitable, emphasizing its pertinence to contemporary air defense needs. The Fuzzy system offers insights into optimal times, types, and launch locations for interceptors or surface-to-air missiles (SAMs) within an integrated air defense system. The research evaluates the defuzzification process using five different methods, with MoM method yielding notably superior results. The incorporation of technology is crucial for ensuring the safety of protected areas, key installations, and the dynamic decision-making processes inherent in air defense systems, necessitating the use of fuzzy logic-based systems.