PAISAR: Predictive Antijam Artificial Intelligence Scanned Array Receivers

Today’s mission-critical communication environments face increasing congestion in the electromagnetic spectrum, resulting in signal loss, distortion, and link degradation. Current state-of-the-art receivers, relying on analog limiters, filters, power-hungry analog-to-digital converters, and advanced digital signal processing, struggle to maintain dynamic range and signal quality in heavily jammed environments. As adversaries become more adept at deploying sophisticated jamming and interference tactics, military communication systems are at risk of being compromised, making it essential to develop more resilient solutions. To address these challenges, we propose an innovative Predictive Antijam Artificial Intelligence Scanned Array Receivers (PAISAR) as next-generation AI-enabled, small SWaP-C active electronically scanned array (AESA) system designed for reliable, adaptive communication in contested environments. PAISAR integrates advanced AI engines, MIMO diversity, and deep learning-based beamforming to automatically detect and mitigate jamming, ensuring secure and resilient communication links. The project’s three key thrusts focus on (1) developing AI-based jamming interference cancellers, (2) expanding antijam capabilities for agile, autonomous communication, and (3) building an end-to-end learning framework to optimize RF frontends and digital beamforming for real-time system adaptability. Through extensive prototyping and field testing, PAISAR will provide next-generation communication solutions, addressing the growing demand for resilient, on-demand antijam technologies for military and tactical applications.