In this paper, we propose a greedy sparse recovery algorithm for target localization with RF sensor networks. The target spatial domain is discretized by grid pixels. When the network area consists only of several targets, the target localization is a sparsity-seeking problem such that the Compressed Sensing (CS) framework can be applied. We cast the target localization as a CS problem and solv...

Abstract: Over the past years, there are increasing interests in recovering the signals from undersampling data where such signals are sparse under some orthogonal dictionary or tight framework, which is referred to be sparse synthetic model. More recently, its counterpart, i.e., the sparse analysis model, has also attracted researcher’s attentions where many practical signals which are sparse ...

This paper considers sufficient conditions for sparse recovery in the sparse multiple measurement vector (MMV) problem for some recently proposed rank aware greedy algorithms. Specifically we consider the compressed sensing framework with random measurement matrices and show that the rank of the measurement matrix in the sparse MMV problem allows such algorithms to reduce the effect of the log ...

We address the problem of robustly recovering the support of high-dimensional sparse signals1 from linear measurements in a low-dimensional subspace. We introduce a new compressed sensing framework through carefully designed sparse measurement matrices associated with low measurement costs and low-complexity recovery algorithms. The measurement system in our framework captures observations of t...

In this work, we propose theoretical and algorithmic-independent recovery conditions which guarantee the uniqueness of block sparse recovery in general dictionaries through a general mixed norm optimization problem. These conditions are derived using the proposed block uncertainty principles and block null space property, based on some newly defined characterizations of block spark, and (p, p)-...