Intelligent Electromagnetic Synthesis: An AI-Driven IoT Framework for Adaptive Antenna Design in Missile Navigation

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 01 | Page :
    By

    Heena T Shaikh,

  • Kazi Kutubuddin Sayyad Liyakat,

  1. Assistant Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Professor and Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

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 Driven IoT, Antenna Design, Missile Navigation, KSK Approach, AI

How to cite this article:
Heena T Shaikh, Kazi Kutubuddin Sayyad Liyakat. Intelligent Electromagnetic Synthesis: An AI-Driven IoT Framework for Adaptive Antenna Design in Missile Navigation. International Journal of Radio Frequency Innovations. 2026; 04(01):-.
How to cite this URL:
Heena T Shaikh, Kazi Kutubuddin Sayyad Liyakat. Intelligent Electromagnetic Synthesis: An AI-Driven IoT Framework for Adaptive Antenna Design in Missile Navigation. International Journal of Radio Frequency Innovations. 2026; 04(01):-. Available from: https://journals.stmjournals.com/ijrfi/article=2026/view=245175


References

  1. Kazi KS. A Study on AI-Driven Internet of Battlefield Things (IoBT)-Based Decision Making: KSK Approach in IoBT. InMerging Artificial Intelligence with the Internet of Things 2025 (pp. 203-238). IGI Global Scientific Publishing.
  2. Aboelazm MA. Integration of AI-Driven Electronic Warfare Radio Monitoring Systems with Antenna Array Direction Finding for Advanced Signal Classification and Localization. In2025 International Telecommunications Conference (ITC-Egypt) 2025 Jul 28 (pp. 503-508). IEEE.
  3. Chaudhary A, Thakur R, Kolhe S, Kamal R. A particle swarm optimization based ensemble for vegetable crop disease recognition. Computers and Electronics in Agriculture. 2020 Nov 1;178:105747.
  4. Bore JJ, Pawar C, Sheikh F, Warbhe M, Reddy KT, Dhale T. Transformative Applications of AI in Antenna Design and Performance Optimization. In2025 3rd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) 2025 Nov 28 (pp. 1-6). IEEE.
  5. Victor MB, Raúl P, Laura CS, Monzo C. AI integration in tactical communication systems and networks: A survey and future research directions. Systems. 2025;13(9):752.
  6. Khan A, Saif AA, Hikmatullah A. AI-Driven Adaptive Communication Framework for Phased Array Radars in Hypersonic Interception. In2025 20th International Conference on Emerging Technologies (ICET) 2025 Nov 18 (pp. 1-6). IEEE.
  7. Chavare SM, Nanaware PP, Wagh SS, Jadhav AT, Yogesh Y, Khadake SB. Smart plant monitoring and automated irrigation system using iot. International Research Journal of Modernization in Engineering, Technology and Science (IRJMETS). 2025 May;7(3):2919-25.
  8. Artyushkin M, Frolov A, Seregin G, Morozov S. Emerging Computational Paradigms for Antenna Design in the Age of Surrogates and AI. In2025 XIХ International Symposium on Problems of Redundancy in Information and Control Systems (Redundancy) 2025 Nov 5 (pp. 1-9). IEEE.
  9. Kilinc NU. Project NUSRET: A Comprehensive Technical Framework for Persistent Electromagnetic Volumetric Denial and the Invisible Air Minefield. Research Gate. 2026 Jan 1.
  10. Wu J, Yu J, Tao Q. Design of a missile-borne conformal microstrip navigation antenna. InMATEC Web of Conferences 2018 (Vol. 232, p. 04080). EDP Sciences.
  11. Ouda AN. A robust adaptive control approach to missile autopilot design. International Journal of Dynamics and Control. 2018 Sep;6(3):1239-71.
  12. Gupta IJ, Weiss IM, Morrison AW. Desired features of adaptive antenna arrays for GNSS receivers. Proceedings of the IEEE. 2016 Mar 29;104(6):1195-206.
Ahead of Print Subscription Review Article
Volume 04
01
Received 22/05/2026
Accepted 23/05/2026
Published 27/05/2026
Publication Time 5 Days
1

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