Correlated Sparse Bayesian Learning for Recovery of Block Sparse Signals with Unknown Borders

We consider the problem of recovering complex-valued block sparse signals with unknown borders. Such signals arise naturally in numerous applications. Several algorithms have been developed to solve the problem of unknown block partitions. In pattern-coupled sparse Bayesian learning (PCSBL), each coefficient involves its own hyperparameter...

Information distances for radar resolution analysis

A stochastic approach to resolution based on information distances computed from the geometry of data models which is characterized by the Fisher information is explored. Stochastic resolution includes probability of resolution and signal-to-noise ratio (SNR). The probability of resolution is assessed from a hypothesis test by exploiting...

Signal-to-Noise-Ratio Analysis of Compressive Data Acquisition

Data acquisition in compressive sensing (CS) is commonly believed to be less complicated, and even less costly, while performing agreeably. There is a major lack of measureable foundations supporting this optimism as the performance and complexity of a CS sensor have hardly been quantified. We aim to fill the gap by computing the performance...

Stochastic resolution analysis of co-prime arrays in radar

Resolution from co-prime arrays and from a full ULA of the size equal to the virtual size of co-prime arrays is investigated. We take into account not only the resulting beam width but also the fact that fewer measurements are acquired by co-prime arrays. This fact is relevant in compressive acquisition typical for compressive sensing. Our...