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首页> 外文会议>Information Sciences and Systems, 2009. CISS 2009 >ExCoV: Expansion-compression Variance-component based sparse-signal reconstruction from noisy measurements
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ExCoV: Expansion-compression Variance-component based sparse-signal reconstruction from noisy measurements

机译：ExCoV：从噪声测量中基于扩展压缩方差分量的稀疏信号重构

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摘要

We present an expansion-compression variance-component based method (EXCOV) for reconstructing sparse or compressible signals from noisy measurements. The measurements follow an underdetermined linear model, with noise covariance matrix known up to a constant. To impose sparse or compressible signal structure, we define high- and low-signal coefficients, where each high-signal coefficient is assigned its own variance, low-signal coefficients are assigned a common variance, and all the variance components are unknown. Our expansion-compression scheme approximately maximizes a generalized maximum likelihood (GML) criterion, providing an approximate GML estimate of the high-signal coefficient set and an empirical Bayesian estimate of the signal coefficients.We apply the proposed method to reconstruct signals from compressive samples, compare it with existing approaches, and demonstrate its performance via numerical simulations.
机译：我们提出了一种基于扩展压缩方差分量的方法（EXCOV），用于从噪声测量中重建稀疏或可压缩信号。测量遵循不确定的线性模型，其中噪声协方差矩阵已知为一个常数。为了施加稀疏或可压缩的信号结构，我们定义了高信号系数和低信号系数，其中为每个高信号系数分配了自己的方差，为低信号系数分配了一个公共方差，并且所有方差成分都是未知的。我们的扩展压缩方案大致最大化了广义最大似然（GML）准则，提供了高信号系数集的近似GML估计和信号系数的经验贝叶斯估计。我们将提出的方法应用于从压缩样本中重构信号，将其与现有方法进行比较，并通过数值模拟证明其性能。

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