Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation

By Zhilin Zhang, Bhaskar D. Rao. IEEE Transactions on Signal Processing, 2012

539 citations recorded by the source corpus.

We examine the recovery of block sparse signals and extend the recovery framework in two important directions; one by exploiting the signals' intra-block correlation and the other by generalizing the signals' block structure. We propose two families of algorithms based on the framework of block sparse Bayesian learning (BSBL). One family, directly derived from the BSBL framework, require knowledge of the block structure. Another family, derived from an expanded BSBL framework, are based on a weaker assumption on the block structure, and can be used when the block structure is completely unknown. Using these algorithms, we show that exploiting intra-block correlation is very helpful in improving recovery performance. These algorithms also shed light on how to modify existing algorithms or design new ones to exploit such correlation and improve performance.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bdb23dfe8e15740bbf928789aecb8a112b5ed6a7

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Citations in the corpus

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning (2012, methodology): “It has been known that exploiting such a block structure can further improve reconstruction performance [14], [17], [18].”
TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise (2014, methodology): “For example, sparse Bayesian learning [30] is an alternative to FOCUSS, and EMD is an alternative to SSA.”
Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, background): “Since the receiver knows the transmitted information data symbols, the problem can be formulated as a block CS model [8], [189], [190], without decorrelating signals from multiusers.”
An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing (2021, unknown): “Let us have a look at one example in [24], where direct sensing is conducted via the block compressive sensing (CS) techniques [36], and the symbols ˜ x q,n,k are used as part of the sensing matrix.”