This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing systems. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived that requires low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of video streaming are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed compressed sensing (CS) approach outperforms existing CS systems.

中文翻译：

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基于概率先验信息的压缩感知系统设计

本文通过利用压缩感知系统的基于概率的先验信息来设计感知矩阵和稀疏恢复算法。根据所使用的字典中每个原子的概率知识，得到一个对角加权矩阵，然后通过最小化加权函数来设计感知矩阵，使得等效字典的 Gram 与字典的 Gram 尽可能接近可能的。推导出相应传感矩阵的解析解，该解需要低计算复杂度。我们还通过稀疏恢复阶段利用此先验信息，并提出了一种概率驱动的正交匹配追踪算法，以提高恢复的准确性。对视频流的合成数据和应用场景进行了模拟，以将所提出的方法与一些现有算法的性能进行比较。结果表明，所提出的压缩感知 (CS) 方法优于现有的 CS 系统。