基于矩阵束与中国剩余定理的真亚奈奎斯特超分辨率方法
Truly Sub-Nyquist Method Based Matrix Pencil and CRT with Super Resolution
AI总结：
本文提出一种真亚奈奎斯特超分辨率方法，融合矩阵束与中国剩余定理，以提升高频信号提取的频率分辨率和精度，并适用于电信、雷达和医学成像等场景。
摘要：
超宽带（UWB）和高吞吐量信号的涌现，推动了数据采样技术的进步。诸如调制宽带转换器（MWC）和压缩自相关频谱感知（CCS）等亚奈奎斯特采样方法，通过以低于奈奎斯特速率的采样来克服传统模数转换器（ADC）的局限性。然而，这些方法面临频谱泄漏和硬件复杂度高等挑战。本文提出一种新颖的超分辨率广义特征值方法，将矩阵束方法与中国剩余定理（CRT）相结合，以在真亚奈奎斯特框架内增强信号处理能力。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价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
