Citation

Sungheetha, Akey and R., Rajesh Sharma and Aroba, Oluwasegun Julius (2026) AI-Enhanced Adaptive Radar Antenna Systems for Next-Generation Earth Observation: A Strategic Innovation Framework for Sustainable Space Technology Transfer. Procedia Computer Science, 282. pp. 810-822. ISSN 18770509

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Abstract

Earth observation systems face critical challenges in achieving real-time adaptive sensing capabilities for dynamic environmental monitoring. Current radar antenna systems lack intelligent reconfiguration mechanisms to optimize data acquisition across varying atmospheric conditions and terrain complexities. This research addresses the technological gap by introducing an AI-Enhanced Adaptive Radar Antenna Framework integrating deep reinforcement learning with neuromorphic processing architectures for autonomous beam steering and signal optimization. The proposed methodology employs a three-tier computational structure: a primary deep learning model for pattern recognition achieving 94.7 percent classification accuracy, a secondary reinforcement learning agent for dynamic beam adjustment with 87.3 percent optimization efficiency, and a tertiary edge computing layer for real-time processing reducing latency to 12.4 milliseconds. Experimental validation using synthetic aperture radar datasets demonstrates 89.6 percent improvement in target detection accuracy compared to conventional systems, with power consumption reduced by 42.8 percent through adaptive resource allocation. The framework processes 2.3 terabytes of observational data per orbit cycle while maintaining 96.2 percent data integrity. Innovation lies in the hybrid neuromorphic-quantum computing interface enabling 3.7-fold acceleration in computational throughput. Results indicate 91.4 percent reliability in adverse weather conditions with beam reconfiguration completed within 8.9 milliseconds. This technology offers significant commercial potential for precision agriculture monitoring, disaster response coordination, and climate change assessment applications, establishing pathways for technology transfer through strategic industry partnerships and sustainable space innovation ecosystems.

Item Type:	Article
Uncontrolled Keywords:	Space technology transfer, Intelligent antenna arrays
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 08:40
Last Modified:	02 Sep 2026 08:40
URII:	http://shdl.mmu.edu.my/id/eprint/16550

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