Stacked Bayesian Matching Pursuit for One-Bit Compressed Sensing

Chae, Jeongmin; Kim, Seonho; Hong, Song‐Nam

doi:10.1109/lsp.2020.2983557

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…At present, the proposed CS reconstruction algorithms mainly include gradient descent method [24], iterative thresholding algorithms [25], orthogonal matching pursuit [26], sparsity adaptive matching pursuit [27], subspace pursuit (SP) [28], stacked Bayesian matching pursuit [29], and sparse Fourier representation method [30].…”

Section: Basic Theory Of the Csmentioning

confidence: 99%

A compressed sensing random measurement matrix construction method: block sparse random measurement matrix

Yu,

Zhang,

Lin

2024

Meas. Sci. Technol.

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Compressed sensing (CS) has shown a huge advantage on data compressing and transmission, and designing a suitable measurement matrix is helpful for performance of the CS. Recently, traditional CS measurement matrices have been well applied in many fields, however, there are still problems, such as long construction time, large storage space, and poor real-time performance. Aiming at above problems, combining the advantages of sparse measurement matrix and identity matrix, a new construction method of measurement matrix named Block Sparse Random Measurement Matrix (BSRMM) is proposed. The proposed matrix satisfies restricted isometry property (RIP) with high probability, has faster construction speed, smaller storage space, and is easy to implement. Finally, the compressed sampling process with the BSRMM is implemented on a wireless sensor node with microprocessor STM32F407, and a good reconstruction effect is achieved on the simulated leak signals from a small gas pipeline network, which verifies the effectiveness of the BSRMM.

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