论文标题
基于模型的分散贝叶斯算法用于分布式压缩感应
Model-based Decentralized Bayesian Algorithm for Distributed Compressed Sensing
论文作者
论文摘要
在本文中，提出了一种基于新型的基于模型的分布式压缩传感（DCS）算法。 DCS利用信号间相关性，并具有共同恢复多个稀疏信号的能力。提出的方法是一种使用1型关节稀疏模型（JSM-1）的贝叶斯分散算法，并利用信号内相关性以及信号间相关性。与仅利用信号的关节稀疏性的常规DCS算法相比，所提出的方法将小波系数之间的尺度内和尺度依赖性考虑在内，以实现单个信号结构的利用。此外，Bessel K形式（BKF）被用作先前的分布，该分布的峰值比高斯分布较高，尾巴较重。差异贝叶斯（VB）推理用于执行后验分布并获得模型参数的封闭式解决方案。模拟结果表明，与最新技术相比，所提出的算法具有良好的恢复性能。
In this paper, a novel model-based distributed compressive sensing (DCS) algorithm is proposed. DCS exploits the inter-signal correlations and has the capability to jointly recover multiple sparse signals. Proposed approach is a Bayesian decentralized algorithm which uses the type 1 joint sparsity model (JSM-1) and exploits the intra-signal correlations, as well as the inter-signal correlations. Compared to the conventional DCS algorithm, which only exploit the joint sparsity of the signals, the proposed approach takes the intra- and inter-scale dependencies among the wavelet coefficients into account to enable the utilization of the individual signal structure. Furthermore, the Bessel K-form (BKF) is used as the prior distribution which has a sharper peak at zero and heavier tails than the Gaussian distribution. The variational Bayesian (VB) inference is employed to perform the posterior distributions and acquire a closed-form solution for model parameters. Simulation results demonstrate that the proposed algorithm have good recovery performance in comparison with state-of the-art techniques.
