Journal Article10.1016/J.SIGPRO.2021.108343
Adaptive Cluster Structured Sparse Bayesian Learning with Application to Compressive Reconstruction for Chirp Signals
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TL;DR: An adaptive cluster structured sparse Bayesian learning algorithm is proposed to alleviate the requirements on the prior knowledge by exploiting and incorporating the local structure of the sparse matrix into the reconstruction model and applies an adaptive mechanism in variable estimation to avoid the model mismatch problem.
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About: This article is published in Signal Processing. The article was published on 01 Jan 2022. The article focuses on the topics: Compressed sensing & Computer science.
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Model-Based Compressive Sensing
TL;DR: In this article, the authors introduce a new class of structured compressible signals along with a new sufficient condition for robust structured compressibility signal recovery that they dub the restricted amplification property, which is the natural counterpart to the restricted isometry property of conventional CS.
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