An Adaptive Bayesian Framework for Recovery of Sources with Structured Sparsity

TL;DR

This paper introduces an adaptive Bayesian sensing framework tailored for recovering structured sparse signals, demonstrating significant improvements over traditional non-adaptive compressive sensing methods through a low-complexity algorithm.

Contribution

It extends adaptive sensing to structured sparsity and develops a low-complexity algorithm based on structured orthogonal sensing, outperforming conventional methods.

Findings

Outperforms non-adaptive compressive sensing with fewer subframes

Effective recovery of structured sparse signals

Low-complexity algorithm based on structured orthogonal sensing

Abstract

In oversampled adaptive sensing (OAS), noisy measurements are collected in multiple subframes. The sensing basis in each subframe is adapted according to some posterior information exploited from previous measurements. The framework is shown to significantly outperform the classic non-adaptive compressive sensing approach. This paper extends the notion of OAS to signals with structured sparsity. We develop a low-complexity OAS algorithm based on structured orthogonal sensing. Our investigations depict that the proposed algorithm outperforms the conventional non-adaptive compressive sensing framework with group LASSO recovery via a rather small number of subframes.