Robust Beamforming Design for Near-Field Integrated Sensing and Communication Systems With Extended Targets

Integrated sensing and communication (ISAC) has become a crucial technology for sixth-generation (6 G) wireless systems. As the scale of antenna arrays increases and operating frequencies rise, the near-field region of base stations (BS) expands significantly, making traditional far-field based ISAC designs insufficient. Perfect channel state information (CSI) is a prerequisite for beamforming, but it is impractical in realworld scenarios. Thus, we consider CSI uncertainty in the communication link, adopt a spherical wavefront model to describe the near-field sensing channel, and develop a robust joint optimization framework to minimize the sensing mean square error (MSE). To solve the resulting non-convex problem efficiently, we apply semidefinite relaxation (SDR) to transform it into a convex semidefinite programming (SDP) problem. Then, Bernstein-type inequalities are employed to safely approximate the probabilistic SINR constraints. Simulation results show that the proposed method achieves sensing performance similar to the non-robust design, while significantly improving communication performance.