The linear inverse source and scattering problems are studied from the perspective of compressed sensing. By introducing the sensor as well as target ensembles, the maximum number of recoverable targets is proved to be at least proportional to the number of measurement data modulo a logsquare factor with overwhelming probability. Important contributions include the discoveries of the threshold aperture, consistent with the classical Rayleigh criterion, and the incoherence effect induced by random antenna locations. The predictions of theorems are confirmed by numerical simulations
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
We consider a compressed sensing problem in which both the measurement and the sparsifying systems a...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
The linear inverse source and scattering problems are studied from the perspective of compressed se...
Compressed sensing is a new data acquisition paradigm enabling universal, simple, and reduced-cost a...
Compressive Sensing is a recently developed technique that exploits the sparsity of naturally occurr...
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to si...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Non-convex constraints have recently proven a valuable tool in many optimisation problems. In partic...
In real-world applications, most of the signals can be approximated by sparse signals. When dealing ...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using...
Compressed sensing has a wide range of applications that include error correction, imaging,...
Whatever the field of application, optimizing the results and sometimes even solving problems requir...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
This chapter is concerned with two important topics in the context of sparse recovery in inverse and...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
We consider a compressed sensing problem in which both the measurement and the sparsifying systems a...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...
The linear inverse source and scattering problems are studied from the perspective of compressed se...
Compressed sensing is a new data acquisition paradigm enabling universal, simple, and reduced-cost a...
Compressive Sensing is a recently developed technique that exploits the sparsity of naturally occurr...
Compressive sensing is a new field in signal processing and applied mathematics. It allows one to si...
Compressed sensing is a novel research area, which was introduced in 2006, and since then has alread...
Non-convex constraints have recently proven a valuable tool in many optimisation problems. In partic...
In real-world applications, most of the signals can be approximated by sparse signals. When dealing ...
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using...
Compressed sensing has a wide range of applications that include error correction, imaging,...
Whatever the field of application, optimizing the results and sometimes even solving problems requir...
The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recover...
This chapter is concerned with two important topics in the context of sparse recovery in inverse and...
This paper introduces a simple and very general theory of compressive sensing. In this theory, the s...
We consider a compressed sensing problem in which both the measurement and the sparsifying systems a...
This paper considers the problem of sparse signal recovery when the decoder has prior information on...