工业控制网络与系统研究室知识库

依据Shannon采样定理的模拟-数字转换器(Analog-to-Digital Convertor,ADC)越来越难以满足对高频、宽频信号的采样需求,为实现低速率采样同时缓解数据传输、存储及处理的压力,基于亚Nyquist采样的模拟信息转换器(Analog-to-Information Convertor,AIC)成为研究热点。本文首先概述了压缩感知(Compressed Sensing,CS)理论、单向量空间(Single Vector Space,SVS)和联合子空间(Union of Subspaces,UoS)采样理论,着重总结和对比了几种符合UoS模型信号的AIC采样架构及恢复算法,进一步提出了一种多天线采样系统架构及基于子空间分解的增强型重构方法,最后展望了AIC未来研究方向。

Traditional analog-to-digital convertors (ADC) that is based on the Shannon sampling theorem can hardly satisfy the sampling requirements for high-frequency and wideband signals. For the purpose of sampling rate reducing, data transmission, storage and process relaxing, new analog-to-information convertors (AIC) which is based on the sub-Nyquist sampling methods have drawn intensive attentions in past few years. In this paper, compressed sensing (CS) theory, single vector space (SVS) and union of subspaces (UoS) sampling theory are first introduced. Based on the UoS signal model, an emphasis on AIC sampling architectures and recovery algorithms are summarized and compared afterwards. Moreover, a new multiple-antenna sub-Nyquist sampling architecture and its corresponding augmented recovery method based on the subspace decomposition are proposed. Finally, future research directions are given.