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中文摘要：
如何降低宽带模拟信号数字化过程中的采样率，以及如何有效的对大量数据进行压缩存储一直是学者们关心的问题。该文综述了最近出现的一种新型信号处理方法一压缩采样（CompressiveSampling，cs），也称压缩传感（CompressiveSensing）。该方法通过对稀疏信号进行观测而非采样，只需少量观测点就能精确的重构原始信号。结果表明新方法的观测频率可以远远低于奈奎斯特采样频率。该文除介绍其基本原理和主要实现方法外，同时列举了多种应用，并指出若干待研究的问题。
英文摘要：
The problems of how to reduce the sampling rate in the broadband analog signal digitization and how to compress effectively the large amount of data for storage are always concerned by researchers. The recent proposed Compressive Sampling or Compressive Sensing method to solve the said problems is introduced in this paper. The method, which employs non-adaptive linear projections that preserve the structure of the signal, can capture and represent the compressible signal at a rate significantly below Nyquist rate. This paper not only presents the key procedures of this theory but also lists a variety of applications and points out the questions to be studied.
同期刊论文项目
同项目期刊论文
A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filter
Proof of convergence and performance analysis for sparse recovery via zero-point attracting projecti
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