To solve the low efficiency of traditional synthetic aperture sonar (SAS) imaging problem, a sonar imaging system combining compressed sensing (CS) and template compressed sensing (TCS) was proposed. The system used sonar network to image scenes. With 10% amount of traditional SAS data, CS and TCS algorithms could recover the image exploiting the structured sparsity of the interested scene with...

Compressed sensing (CS) and 1-bit CS cannot directly recover quantized signals and require time consuming recovery. In this paper, we introduce Hamming compressed sensing (HCS) that directly recovers a k-bit quantized signal of dimensional n from its 1-bit measurements via invoking n times of Kullback-Leibler divergence based nearest neighbor search. Compared with CS and 1-bit CS, HCS allows th...

Abstract: Compressed sensing (CS) has become a powerful tool to process data that is correlated in underwater sensor networks (USNs). Based on CS, certain signals can be recovered from a relatively small number of random linear projections. Since the battery-driven sensor nodes work in adverse environments, energy-efficient routing well-matched with CS is needed to realize data gathering in USN...

This paper first present a new general completely perturbed compressed sensing (CS) model y=(A+E)(x+u)+e,called noise folding based on general completely perturbed CS system, where y ∈ Rm, u ∈ Rm, u 6= 0, e ∈ Rm, A ∈ Rm×n, m ≪ n, E ∈ Rm×n with incorporating general nonzero perturbation E to sensing matrix A and noise u into signal x simultaneously based on the standard CS model y=Ax+e. Our cons...

Introduction Compressed Sensing (CS) (e.g. [1]) is a novel approach to reconstruct sparse undersampled datasets. Several papers (e.g. [2]) have demonstrated the benefit of CS in the field of MRI. Nonconvex CS [3] is a more recent development which allows for even higher acceleration factors and is easy to implement. A first application for dynamic cardiac imaging has been demonstrated [4]. Howe...

The compressed sensing (CS) theory is a novel way to break through the existent difficulty in ultra-wideband jamming method development. In this paper, the application of the CS theory in linear frequency modulated signal processing is introduced, a new retransmitted jamming system based on CS is designed with its composition and workflow. Then, two generation modes of jamming signal are illust...

INTRODUCTION Considerable attention has been paid to compressed sensing (CS) in the MRI community recently (1,2). CS theory allows exact recovery of a sparse signal from a highly incomplete set of samples (3,4), and thus has the potential for significant reduction in MRI scan time. While most existing work has focused on Fourier encoding, non-Fourier encoding has shown some promise (5,6). In th...

The emergence of compressed sensing (CS) theory provides potential hardware architecture to sub-Nyquist sample the wideband signals. However, applying this discrete CS model to continuous analogue signals is not an easy task. The modulated wideband converter (MWC) is an efficient wideband compressed sampling architecture for the sparse multiband signals. In this paper, a soft-calibration system...

Sub-Nyquist sampling techniques for Wireless Sensor Networks (WSN) are gaining increasing attention as an alternative method to capture natural events with desired quality while minimizing the number of active sensor nodes. Among those techniques, Compressive Sensing (CS) approaches are of special interest, because of their mathematically concrete foundations and efficient implementations. We d...

We consider the problem of recursively reconstructing time sequences of sparse signals (with unknown and time-varying sparsity patterns) from a limited number of linear incoherent measurements with additive noise. The idea of our proposed solution, KF CS-residual (KFCS) is to replace compressed sensing (CS) on the observation by CS on the Kalman filtered (KF) observation residual computed using...