The emergence of ultra-wideband (UWB) and high-throughput signals has necessitated advancements in data sampling technologies1. Sub-Nyquist sampling methods, such as the modulated wideband converter (MWC) and compressed auto-correlation spectrum sensing (CCS), address the limitations of traditional analog-to-digital converters (ADCs) by capturing signals below the Nyquist rate. However, these methods face challenges like spectral leakage and complex hardware requirements. This paper proposes a novel super-resolution generalized eigenvalue method that integrates the matrix pencil method with the Chinese Remainder Theorem (CRT) to enhance signal processing capabilities within a true sub-Nyquist framework3. This approach aims to improve frequency resolution and accuracy in high-frequency signal extraction, with potential applications in telecommunications, radar, and medical imaging.

翻译：超宽带（UWB）和高吞吐量信号的出现推动了数据采样技术的进步。亚奈奎斯特采样方法，如调制宽带转换器（MWC）和压缩自相关频谱感知（CCS），通过以低于奈奎斯特率的速率捕获信号，解决了传统模数转换器（ADC）的局限性。然而，这些方法面临着频谱泄漏和复杂硬件要求等挑战。本文提出了一种新颖的超分辨率广义特征值方法，该方法将矩阵铅笔法与中国剩余定理（CRT）相结合，以增强真亚奈奎斯特框架内的信号处理能力。该方法旨在提高高频信号提取中的频率分辨率和精度，在电信、雷达和医学成像等领域具有潜在应用前景。