Single-channel sampling and multi-channel reconstruction AIC via multiple chirp noise sequences
Single-channel sampling and multi-channel reconstruction AIC via multiple chirp noise sequences
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DOI：
10.1007/s11432-018-9471-3
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Pengfei Shi;H. Huan;R. Tao
中科院分区：
文献类型：
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作者：
Pengfei Shi;H. Huan;R. Tao
作为压缩感知（CS）的初步实现，模拟-信息转换器（AIC）在通过PN序列降低模拟信号的采样率方面起着重要作用[1，2]。然而，在低信噪比（SNR）情况下，在测量前获取含噪信号时会产生严重的重构误差（RE）[3]。为了抑制RE，在[4]中首次引入了自适应加权范数约束，使加权l1范数约束模型能够获得l0范数模型的精确逼近。蒸馏传感用于通过设计自适应测量来控制噪声折叠，以检测和定位加性白色高斯噪声（AWGN）环境中的弱信号[5]。此外，通过将l1最小化与正则化选择性最小p幂或迭代硬阈值结合，提出了两种新类型的解码过程[6]。同时，在恢复算法中初步考虑了数据预处理操作，如[7]中的自适应选择性压缩采样（ASCS）。这些关于RE的相关研究主要集中在稀疏信号及其噪声上，然而，在RE中起主导作用的是非线性重构矩阵[8]。
Dear editor, As preliminary implementation of compressed sensing (CS), the analog-to-information converter (AIC) plays an important role in reducing the sampling rate of analog signals via PN sequence [1, 2]. However, it suffers severe reconstruction errors (REs) when acquiring noisy signals prior to measurement in cases with low signal-noise ratio (SNR)[3].To damp the RE, the adaptive weighted norm constraint is first introduced in [4], which enables the weighted l1 norm constrained model obtain an accurate approximation of the l0 norm model. Distilled sensing is used to control the noise folding by designing adaptive measurements to detect and locate weak signals in the additive white Gaussian noise (AWGN) environment [5]. Further, two new-types of decoding procedures are proposed by combining l1-minimization with either a regularized selective least p-powers or an iterative hard thresholding [6]. Meanwhile, a data pre-processing operation, such as the adaptive selective compressive sampling (ASCS) in [7], is preliminarily considered in the recovery algorithm. These relative studies on the RE are mainly focused on the sparse signal and its noise; however, it is the nonlinear reconstruction matrix that plays the dominant role in the RE [8].