  This paper considers a joint communication and sensing technique for
enhancing situational awareness in practical battlefield scenarios. In
particular, we propose an aerial reconfigurable intelligent surface
(ARIS)-assisted integrated sensing and communication (ISAC) system consisting
of a single access point (AP), an ARIS, multiple users, and a sensing target.
With deep reinforcement learning (DRL), we jointly optimize the transmit
beamforming of the AP, the RIS phase shifts, and the trajectory of the ARIS
under signal-to-interference-noise ratio (SINR) constraints. Numerical results
demonstrate that the proposed technique outperforms the conventional benchmark
schemes by suppressing the self-interference and clutter echo signals or
optimizing the RIS phase shifts.
