Sensing-Driven Secure Transmission with Mobile Eavesdropper Tracking in RIS-Assisted ISAC
TL;DR
A multi-view angle estimation method jointly exploiting the base station (BS) and RIS is developed based on MUSIC and maximum likelihood estimation (MLE), enabling three-dimensional Eve localization, whose accuracy is characterized by the Cramer-Rao lower bound (CRLB).
Abstract
The mobility of potential eavesdroppers (Eves) poses a critical challenge to physical-layer security (PLS) in integrated sensing and communication (ISAC) systems, since rapidly varying Eve positions lead to time-varying eavesdropping channels and outdated security decisions. To address this challenge, this paper investigates sensing-driven secure transmission for reconfigurable intelligent surface (RIS)-assisted ISAC systems with a maneuvering Eve. To this end, by exploiting the dual functionality of the ISAC waveform, a multi-view angle estimation method jointly exploiting the base station (BS) and RIS is developed based on MUSIC and maximum likelihood estimation (MLE), enabling three-dimensional (3D) Eve localization, whose accuracy is characterized by the Cramer-Rao lower bound (CRLB). Subsequently, an interacting multiple model extended Kalman filter (IMM-EKF) with out-of-sequence measurement (OOSM) compensation is employed to track and predict the maneuvering Eve. Based on the predicted trajectory, the time-varying eavesdropping channel is updated, and the transmit beamforming and RIS phase shifts are jointly optimized subject to a sensing CRLB constraint. Furthermore, an alternating optimization (AO) algorithm integrating fractional programming, semidefinite relaxation (SDR), and Riemannian manifold optimization is developed to solve the resulting non-convex problem. Finally, numerical results demonstrate the effectiveness of the proposed approach in accurately tracking maneuvering Eves and enhancing secure transmission in dynamic ISAC environments.