Intelligent Electromagnetic Synthesis: An AI-Driven IoT Framework for Adaptive Antenna Design in Missile Navigation - Heena T Shaikh; Kazi Kutubuddin Sayyad Liyakat (International Journal of Radio Frequency Innovations, 2026)
The rapid evolution of hypersonic and long-range tactical missile systems necessitates antenna architecture capable of maintaining robust communication links under extreme thermal, mechanical, and signal-jamming environments. Traditional antenna design methodologies often relying on iterative simulation cycles and static optimization are increasingly insufficient for the real-time requirements of modern aerospace navigation. This paper proposes an AI-driven, IoT- integrated framework that facilitates autonomous antenna design and performance optimization. By deploying a distributed network of IoT sensors across the missile’s airframe, real-time environmental data (including plasma sheath turbulence, aerodynamic heating, and structural deformation) is harvested and fed into a deep reinforcement learning (DRL) engine. This system autonomously adjusts the antenna’s electromagnetic characteristics such as impedance matching, beam steering, and frequency agility to maintain a stable Global Navigation Satellite System (GNSS) lock. Simulation results demonstrate that this closed-loop, AI-IoT integration reduces design iteration time by 60% and improves signal-to-noise ratio (SNR) by 14 dB compared to conventional fixed-geometry antenna systems, significantly enhancing the precision and survivability of missile navigation in contested electromagnetic spectra. In the future of missile navigation, the antenna stops being a passive component and becomes the most intelligent part of the vehicle an electronic eye that sees through the noise, adapting in real-time to ensure the path forward is always clear.
Keywords: AI; AI driven IoT; Antenna design; KSK approach; Missile Navigation