The finite-rate-of-innovation (FRI) sampling frame has drawn a great deal of attention in many applications. In this paper, a novel sub-Nyquist FRI-based sampling and reconstruction method in linear canonical transform (LCT) domain is proposed. First, a new, compact-support sampling kernel is designed to acquire sub-Nyquist samples in time domain, which can be viewed as anti-aliasing prefilter in LCT domain. Then, the corresponding sampling theorem is derived and the reconstruction algorithm is summarized based on annihilating filter and least square method. Moreover, compared with other representative sub-Nyquist sampling methods, the experiment results demonstrate the superior reconstruction performance of the proposed method. The reconstruction ability in noisy environment is also measured by mean square error. Finally, the proposed method is applied to time delay estimation and can obtain super-resolution results.

有限创新率 (FRI) 抽样框架在许多应用中引起了极大的关注。在本文中，提出了一种新的基于子奈奎斯特 FRI 的线性规范变换 (LCT) 域中的采样和重建方法。首先，设计了一个新的、紧凑支持的采样内核来获取时域中的亚奈奎斯特样本，可以将其视为 LCT 域中的抗混叠预滤波器。然后，推导出相应的采样定理，并总结出基于湮灭滤波器和最小二乘法的重构算法。此外，与其他有代表性的亚奈奎斯特采样方法相比，实验结果证明了该方法具有优越的重建性能。噪声环境下的重建能力也用均方误差来衡量。最后，