a r t i c l e i n f o a b s t r a c t Article history: Available online xxxx Keywords: High resolution Ranging method Low-rate random sampling Compressed sensing Sparse representation In this paper, we propose a low-rate high-resolution ranging method for UWB (up to several GHz of sampling rate) ranging system. It exploits compressed sensing (CS) theory and a parallel sampling ADCs structure ba...

We present Adaptive Direct Sampling (ADS), an improved algorithm for simultaneous image acquisition and compression which does not require the data to be sampled at its highest resolution. In some cases, our approach simplifies and improves upon the existing methodology of Compressed Sensing (CS), by replacing the ‘universal’ acquisition of pseudo-random measurements with a direct and fast meth...

We present Adaptive Direct Sampling (ADS), an algorithm for image acquisition and compression which does not require the data to be sampled at its highest resolution. In some cases, our approach simplifies and improves upon the existing methodology of Compressed Sensing (CS), by replacing the ‘universal’ acquisition of pseudo-random measurements with a direct and fast method of adaptive wavelet...

Compressed sensing is a new sampling technique which can exactly reconstruct sparse signal from a few measurements. In this article, we consider the blocksparse compressed sensing with special structure assumption about the signal. A novel non-convex model is proposed to reconstruct the block-sparse signals. In addition, the conditions of the proposed model for recovering the block-sparse noise...

Due to their noise-like features, SAR images are difficult to acquire with compressed sensing techniques. However, some parts of the images, typically associated to man-made structures, are compressible and we investigate two techniques exploiting that information to allow a compressive acquisition of the whole image. These techniques result in a significant enhancement of the image quality com...

Noncoherent detectors significantly contribute to the practical realization of the ultrawideband (UWB) impulse-radio (IR) concept, in that they allow avoiding channel estimation and provide highly efficient reception capabilities. Complexity can be reduced even further by resorting to an all-digital implementation, but Nyquist-rate sampling of the received signal is still required. The current ...

Compressed sensing (CS) or compressive sampling provides a new sampling theory to reduce data acquisition, which says that compressible signals can be exactly reconstructed from highly incomplete sets of measurements. Very recently, the CS has been applied for seismic exploration and started to compact the traditional data acquisition. In this paper, we present an optimized sampling strategy fo...

Consider a Gaussian memoryless multiple source with m components with joint probability distribution known only to lie in a given class of distributions. A subset of k ≤ m components are sampled and compressed with the objective of reconstructing all the m components within a specified level of distortion under a meansquared error criterion. In Bayesian and nonBayesian settings, the notion of u...

Compressed sensing has become an extensive research area in MR community because of the opportunity for unprecedented high spatio-temporal resolution reconstruction. Because dynamic magnetic resonance imaging (MRI) usually has huge redundancy along temporal direction, compressed sensing theory can be effectively used for this application. Historically, exploiting the temporal redundancy has bee...

We address the problem of parameter estimation of superimposed chirp signals in noise. The approach used here is a computationally modest implementation of a maximum likelihood (ML) technique. The ML technique for estimating the complex amplitudes, chirping rates, and frequencies reduces to a separable optimization problem where the chirping rates and frequencies are determined by maximizing a ...