Authors: Sanjay Singh, Manas Bajpai
Abstract: Modern battlefields demand real-time intelligence and rapid decision-making in complex environments. This paper proposes an AI-assisted multi-sensor fusion architecture deployed on FPGA-based Unmanned Aerial Vehicles (UAVs) for enhanced situational awareness. The system integrates data from hetero- geneous sensors—thermal, acoustic, visual, and radar—through a lightweight fusion algorithm optimized for FPGA implementa- tion. The use of adaptive AI-driven fusion enables low-latency, power-efficient and reliable detection of enemy drones, impro- vised explosive devices (IEDs) and human activity. Experimental simulations demonstrate significant improvements in detection accuracy and response time compared to conventional centralized systems.
DOI: https://doi.org/10.5281/zenodo.17528750
