Compressive Sensing (CS) has demonstrated to be particularly adapt for dealing with the directions-of-arrival (DoAs) estimation of electromagnetic signals impinging on an array of sensors. Unlike deterministic CS methods, the Bayesian CS (BCS) allows to overcome some theoretical limitations of the CS, such to enable a reliable and versatile DoAs estimation tool able to work with different array...

This paper represents the reconstruction of sampled signal in CS by using OMP algorithm. We have used the concept of compressive sensing for sub Nyquist sampling of sparse signal. Compressive sensing reconstruction methods have complex algorithms of l1 optimisation to reconstruct a signal sampled at sub nyquist rate. But out of those algorithm OMP algorithm is fast and computationally efficient...

Recall the setup in compressive sensing. There is an unknown signal z ∈ R, and we can only glean information about z through linear measurements. We choose m linear measurements a1, . . . , am ∈ R. “Nature” then chooses a signal z, and we receive the results b1 = 〈a1, z〉, . . . , bm = 〈am, z〉 of our measurements, when applied to z. The goal is then to recover z from b. Last lecture culminated i...

Infrared target detection research has recently attracted much attention, especially in pedestrian detection. In this paper, we use compressive sensing theory, a new and emerging field, for an infrared target detection system. Based on the framework of Bayesian filter, target is expressed by sparse representation. Compressive sensing (CS) is based on the illusion that a small quantity of non-ad...

Protection of biometric template against spoofing or modification attack at system database is major issue in multibiometric system. Hence fragile digital watermarking technique is one of the solutions for biometric template protection against these attacks. In this paper, fingerprint watermarking technique based on SVD and Compressive Sensing theory proposed for protection of biometric templat...

Abstract—Circular synthetic aperture radar (CSAR) imaging based on compressive sensing with random step frequency (RSF) as transmitted signal is introduced. CSAR is capable of obtaining both two-dimensional high resolution image and three-dimensional image due to a circular collection trajectory. RSF signal shares good characteristics of noise signals including “thumbtack-shape” ambiguity funct...

The WSNs are developed to sense, gather, process and transmit the real-world information and so in recent years, solving numerous challenges of wireless sensor networking are considered intensely. In addition, in Wireless Multimedia Sensor Networks intraand intersignal correlations can be exploited in the theory of distributed source coding and similarly in distributed compressive sensing to co...

Cost-efficient compressive sensing of big media data with fast reconstructed high-quality results is very challenging. In this paper, we propose a new large-scale image compressive sensing method, composed of operator-based strategy in the context of fixed point continuation method and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any as...

In many compressive sensing problems today, the relationship between the measurements and the unknowns could be nonlinear. Traditional treatment of such nonlinear relationships have been to approximate the nonlinearity via a linear model and the subsequent un-modeled dynamics as noise. The ability to more accurately characterize nonlinear models has the potential to improve the results in both ...