Robust transceiver beamforming scheme for multi-UAV-enabled integrated sensing and communication systems

Driven by the various sensing demands, integrated sensing and communication (ISAC) is considered as a promising technique in further wireless network. In this paper, we propose a robust transceiver beamforming scheme for multiple unmanned-aerial vehicles (UAVs)-enabled ISAC system to enhance both communication and radar sensing performance. Here, each UAV communicates with the base station (BS) and performs radar sensing for one target in the presence of multiple clutters. In particular, to guarantee robustness against channel uncertainty, we employ the imperfect channel state information (CSI) and formulate a joint optimization problem to maximize the minimal achievable rate of UAVs, subject to the constraints of the signal-to-clutter plus interference and noise ratio requirement and the UAV transmit power budget. To handle the impact of channel uncertainty, we leverage the triangle inequality and Kronecker product properties to transform the worst-case constraints into tractable forms, ensuring robustness against CSI errors. Then, we propose an alternating optimization framework based on semidefinite programming to iteratively optimize transceiver beamformers. Numerical results are provided to demonstrate the robustness and effectiveness of the proposed joint optimization scheme in terms of achievable rate performance.