The Bayesian compressive sensing algorithm is utilized together with the method of moments to fast analyze the monostatic electromagnetic scattering problem. Different from the traditional compressive sensing based fast monostatic scattering analysis method which cannot determine the required measurement times, the proposed method adopts the Bayesian framework to recover the underlying signal. Error bars of the signal can be obtained in the recovery procedure, which provides a means to adaptively determine the number of compressive-sensing measurements. Numerical results are given to demonstrate the accuracy and effectiveness of proposed method
An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-spars...
This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal ...
International audienceThis paper investigates an innovative imaging approach for reconstructing 3D o...
In this paper, a new CS-FMM method that conjugates compressive sensing (CS) with the fast multipole ...
International audienceThe Bayesian retrieval of sparse scatterers under multifrequency transverse ma...
In recent years, Compressive Sensing (CS) theory has been very popular in the data sensing and proce...
A hybrid compressive approach for fast computation of the electromagnetic scattering problems with m...
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to si...
This thesis deals with an emerging area of signal processing, called Compressive Sensing (CS), that ...
International audienceThe paradigm of Compressive Sensing (CS) has emerged in the last few years as ...
Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to ...
International audienceThe paradigm of Compressive Sensing (CS) has emerged in the last few years as ...
In this paper, we propose a Bayesian compressive sensing algorithm for effective reconstruction of s...
Abstract—We propose a Bayesian based algorithm to recover sparse signals from compressed noisy measu...
The theory and application of compressive sensing (CS) have received a lot of interest in recent yea...
An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-spars...
This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal ...
International audienceThis paper investigates an innovative imaging approach for reconstructing 3D o...
In this paper, a new CS-FMM method that conjugates compressive sensing (CS) with the fast multipole ...
International audienceThe Bayesian retrieval of sparse scatterers under multifrequency transverse ma...
In recent years, Compressive Sensing (CS) theory has been very popular in the data sensing and proce...
A hybrid compressive approach for fast computation of the electromagnetic scattering problems with m...
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to si...
This thesis deals with an emerging area of signal processing, called Compressive Sensing (CS), that ...
International audienceThe paradigm of Compressive Sensing (CS) has emerged in the last few years as ...
Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to ...
International audienceThe paradigm of Compressive Sensing (CS) has emerged in the last few years as ...
In this paper, we propose a Bayesian compressive sensing algorithm for effective reconstruction of s...
Abstract—We propose a Bayesian based algorithm to recover sparse signals from compressed noisy measu...
The theory and application of compressive sensing (CS) have received a lot of interest in recent yea...
An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-spars...
This chapter provides the use of Bayesian inference in compressive sensing (CS), a method in signal ...
International audienceThis paper investigates an innovative imaging approach for reconstructing 3D o...