Nonlinear sampling for sparse recovery

S. A. Hosseini, Mahdi Barzegar Khalilsarai, A. Amini, F. Marvasti
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Abstract

Linear sampling of sparse vectors via sensing matrices has been a much investigated problem in the past decade. The nonlinear sampling methods, such as quadratic forms are also studied marginally to include undesired effects in data acquisition devices (e.g., Taylor series expansion up to two terms). In this paper, we introduce customized nonlinear sampling techniques that provide possibility of sparse signal recovery. The main advantage of the nonlinear method over the conventional linear schemes is the reduction in the number of required samples to 2k for recovery of k-sparse signals. We also introduce a low-complexity reconstruction method similar to the annihilating filter in the sampling of signals with finite rate of innovation (FRI). The disadvantage of this nonlinear sampler is its sensitivity to additive noise; thus, it is suitable in scenarios dealing with noiseless data such as network packets, where the data is either noiseless or it is erased. We show that by increasing the number of samples and applying denoising techniques, one can improve the performance. We further introduce a modified version of the proposed method which has strong links with spectral estimation methods and exhibits a more stable performance under noise and numerical errors. Simulation results confirm that this method is much faster than ℓ1-norm minimization routines, widely used in linear compressed sensing and thus much less complex.
稀疏恢复的非线性采样
利用传感矩阵对稀疏向量进行线性采样是近十年来研究的一个热点问题。非线性采样方法,如二次型也进行了研究,以包括数据采集设备中的不良影响(例如,泰勒级数展开到两项)。在本文中,我们介绍了定制的非线性采样技术,提供了稀疏信号恢复的可能性。与传统的线性方案相比,非线性方法的主要优点是将恢复k-稀疏信号所需的样本数量减少到2k。在有限创新率(FRI)信号的采样中,我们还引入了一种类似于湮灭滤波器的低复杂度重构方法。这种非线性采样器的缺点是对加性噪声敏感;因此,它适用于处理诸如网络数据包之类的无噪声数据的场景,其中数据要么是无噪声的,要么是被擦除的。我们表明,通过增加样本数量和应用去噪技术,可以提高性能。我们进一步介绍了一种改进的方法,该方法与谱估计方法有很强的联系,在噪声和数值误差下表现出更稳定的性能。仿真结果表明,该方法比线性压缩感知中广泛使用的1-范数最小化方法要快得多,因此复杂性大大降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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