正交时频空间（orthogonal time frequency space， OTFS）调制通信系统的性能严重依赖于信道状态信息的获取精度。利用时延-多普勒域的稀疏性，OTFS信道估计问题可转化为二维稀疏表示问题，但二维求解的计算复杂度极高，且现有方法普遍无法有效对抗分数多普勒/时延误差。为应对上述挑战，提出一种基于非均匀网格的稀疏表示方法，将OTFS信道估计问题建模成一个较低维度的稀疏信号恢复问题，利用稀疏贝叶斯学习推断出最大后验意义上的最优解，以较低的计算复杂度实现了二维稀疏信号的恢复。此外，各网格点坐标被视为可调变量，自适应迭代抵消分数多普勒/时延的影响。仿真结果表明，所提方法可有效提高OTFS信道估计的性能。

The acquisition of channel state information (CSI) has a significant impact on the performance of orthogonal time frequency space (OTFS) modulated communication systems. By exploiting the sparsity in the delay-Doppler domain, the channel estimation problem of OTFS can be transformed into a two-dimensional sparse representation problem. However, the computational complexity of solving the two-dimensional sparse signal is extremely high, and existing methods generally cannot effectively against errors caused by fractional Doppler/delay. To address these issues, we propose a non-uniform grid-based sparse representation method to model the OTFS channel estimation as a lower-dimensional sparse signal recovery problem. Sparse Bayesian learning is then used to infer the optimal solution in the sense of maximum a posteriori, achieving two-dimensional sparse signal recovery with low computational complexity. To counteract the fractional Doppler/delay errors, we treat the coordinates of each grid point as adjustable variables and iterate them adaptively. Simulation results illustrate that the proposed method can effectively improve the performance of OTFS channel estimation.