Fluid antenna systems (FASs) can reconfigure their antenna locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance loss. In this paper, we propose the successive Bayesian reconstructor (S-BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S-BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S-BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as the maximum a posterior (MAP) estimator of FAS channels. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.

翻译：流体天线系统能够在连续空间内自由重构天线位置。为保持最佳天线位置，获取流体天线系统的信道状态信息至关重要。尽管已有若干技术被提出，但现有大多数流体天线信道估计器需要多种信道假设，如慢变特性和角度域稀疏性。当这些假设不成立时，模型失配可能导致不可预测的性能损失。本文提出连续贝叶斯重构器作为流体天线信道估计的通用解决方案。不同于基于模型的估计器，所提S-BAR采用先验辅助方式，为信道状态信息获取构建经验核函数。受贝叶斯回归启发，S-BAR的核心思想是将流体天线信道建模为随机过程，通过基于核函数的采样与回归，其不确定性可被连续消除。这种机制下，回归随机过程的预测均值可视为流体天线信道的最大后验估计器。仿真结果表明，在模型失配与模型匹配两种场景下，所提S-BAR的估计精度均优于现有方案。