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Extremely large-scale antenna arrays (ELAAs) are indispensable to meet the elevated key performance indicators (KPIs) for the sixth generation (6G) networks while leading to new challenges in the exploration of near-field system models. In response, we propose a near-field integrated sensing and communications (ISAC) system aided by an extremely large-scale simultaneously transmitting and reflecting reconfigurable intelligent surface (XL-STAR-RIS) and the fluid antenna (FA) in this paper. To precisely estimate the distance and angle of arrival (AoA) of the target while concurrently ensuring effective signal transmission to communication users, we formulate a joint minimization problem for Cramér-Rao bounds (CRBs) of the distance and AoA estimation mean square errors (MSEs). By optimizing the communication beamformer, the sensing signal covariance matrix, the XL-STAR-RIS phase shift, and the FA position vector, CRBs are minimized while ensuring desired communication performance. A double-loop iterative algorithm based on the penalty dual decomposition (PDD) and block coordinate descent (BCD) method is proposed to solve the non-convex problem by decomposing it into three subproblems and optimizing the coupling variables iteratively. Simulation results validate the superior performance of the proposed algorithm.
Zhou et al. (Tue,) studied this question.