Robust and Adaptive Beamforming for Covert ISAC Under Dual Uncertainties

This paper addresses the challenge of designing beamforming (a technique for directing radio signals in specific directions) for covert integrated sensing and communication (ISAC, a system that combines radar and wireless communication functions) when an eavesdropper has incomplete information about the communication channel and noise levels. The researchers develop two optimization methods: a robust optimization approach for stable environments and a machine learning-based algorithm for rapidly changing conditions, both designed to maximize radar performance while keeping communications hidden from eavesdroppers.