Frequency-domain adaptive sparse signal reconstruction at sub-Nyquist rate
Frequency-domain adaptive sparse signal reconstruction at sub-Nyquist rate
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DOI：
10.1109/iccchina.2016.7636810
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Beiyi Liu;Guan Gui;Ying Zhu;Li Xu
中科院分区：
文献类型：
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作者：
Beiyi Liu;Guan Gui;Ying Zhu;Li Xu
亚奈奎斯特稀疏信号重构技术可以显著降低硬件设计成本。许多次Nyquist重构算法(如贪婪松弛算法和凸优化算法)已被用来利用实频稀疏信号的稀疏性进行重构。然而，贪婪算法需要较大的内存，而凸优化算法耗费较长的计算时间。与以往的稀疏信号重构方案不同，本文提出了一种次奈奎斯特下的频域自适应稀疏信号重构方案，以获得更好的性能和更少的计算时间。具体地，采用离散Hartley变换(DHT)在频域寻找具有亚奈奎斯特随机解调采样率的稀疏表示，并利用GB0-NLMS算法重构稀疏信号。实验结果证实了该方法在计算时间和均方误差方面的优势。
Sub-Nyquist sparse signal reconstruction technique can significantly reduce the cost of hardware design. Many sub-Nyquist reconstruction algorithms (e.g., greedy relax and convex optimization) have been developed to reconstruct the real frequency-sparse signal by utilizing its sparsity. However, greedy algorithms require a large memory size and convex optimization algorithms exhaust a long calculation time. Unlike previous schemes, in this paper, we propose a frequency-domain adaptive sparse signal reconstruction scheme under sub-Nyquist to achieve better performance and lower computational time. Specifically, discrete Hartley transform (DHT) is adopt to find a sparse representation in frequency domain accompanying with sub-Nyquist random demodulation sampling rate and £0-NLMS algorithm is utilized to reconstruct sparse signal. Experiment results are conducted to confirm the advantages of the proposed method in terms of computational time and mean square error.