Adaptive Cluster Structured Sparse Bayesian Learning with Application to Compressive Reconstruction for Chirp Signals

Wang, Qiang; Meng, Chen; Wang, Cheng; Wang, Lu

doi:10.1016/j.sigpro.2021.108343

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…Compressive sensing theory is able to accurately reconstruct high-dimensional signals with good sparse representations from a small number of non-adaptive linear measurements, which breaks the limitations of the Nyquist sampling theorem on the sampling process. It enriches the method of signal sampling theory and is widely used in fields such as broadband signal acquisition [7], medical imaging [8], and data compression [9], with great development prospects.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Bipolar Toeplitz Measurement Matrix Based on Cosine-Exponential Chaotic Map and Improved Abolghasemi Algorithm

Meng,

Wang

et al. 2023

RADIOENGINEERING

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In compressive sensing theory, the measurement matrix plays a crucial role in compressive observation of sparse signals. The bipolar Toeplitz measurement matrix constructed based on chaotic map has advantages such as generating fewer free elements and supporting fast algorithms, making it widely used. While optimizing the measurement matrix can effectively improve its compressive sensing reconstruction performance, existing optimization algorithms are not suitable for the bipolar Toeplitz measurement matrix due to its structural and bipolar properties. To address this issue, this paper proposes an optimization method for the bipolar Toeplitz measurement matrix based on cosine-exponential (CE) chaotic map sequences and an improved Abolghasemi algorithm. Using an enhanced CE chaotic map to generate chaotic sequences with greater chaos and randomness, we construct the measurement matrix and optimize it using the structure matrix and the improved Abolghasemi algorithm, which preserves the matrix's bipolarity without altering its structure. We also introduce constraints on the generated sequence values during the optimization process. Through simulation experiments, the effectiveness of our optimization algorithm is verified, as the optimized bipolar Toeplitz measurement matrix significantly reduces reconstruction error and improves reconstruction probability.

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Section: Introductionmentioning

confidence: 99%