Artificial Noise Aided UAV-ISAC System Against Malicious Radar Signal Detection and Communication Eavesdropping

Abstract

In this paper, a novel artificial noise (AN)-aided secure and covert integrated sensing and communication (ISAC) framework is established for uncrewed aerial vehicle (UAV) systems, to against malicious radar signal detection and communication eavesdropping. Specifically, we consider that besides the communication and sensing signals, the AN signal, which is used to interfere with the eavesdropper and conceal the existence of radar signal, will be transmitted by the UAV-enabled base station (UBS) with uncertainty on its power level. The closed-form expressions of intercept probability (IP) as well as the minimum detection error probability (M-DEP) are derived. Moreover, an efficient communication and sensing performance maximization strategy is designed by optimizing the beamforming vector of communication, covariance matrix of sensing, and UBS receiver filter jointly, to satisfy the IP, power and M-DEP constraints. Simulation results are provided to verify the effectiveness of our joint design by comparing it to benchmark strategy. Moreover, the impact of AN power uncertainty is examined via simulations.