Process / pipelineSub-Nyquist acquisition
压缩感知
压缩感知（Compressive Sensing, CS）是一种信号采集与重建技术，它利用信号的稀疏性，以远低于奈奎斯特采样定理所需的采样数，从少量测量中恢复高分辨率信号。该技术由Emmanuel Candès、Justin Romberg和Terence Tao于2006年提出，通过证明具有稀疏表示的信号可以通过非线性优化从亚奈奎斯特随机测量中重建，从而挑战了传统的采样范式。
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来源
- Candes, E. J., Romberg, J., & Tao, T. (2006). Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete and Inaccurate Measurements. IEEE Transactions on Information Theory, 52(2), 489–509. DOI: 10.1109/TIT.2005.862083 ↗
- Eldar, Y. C., & Kutyniok, G. (2012). Compressed Sensing: Theory and Applications. Cambridge University Press. link ↗
如何引用本页
ScholarGate. (2026, June 3). Compressive Sensing (Compressed Sensing) Signal Acquisition. ScholarGate. https://scholargate.app/zh/signal-processing/compressive-sensing
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