The exploitation of sparsity has significantly advanced the field of radar imaging over the last few decades, leading to substantial improvements in the resolution and quality of the processed images. More recent developments in compressed sensing (CS) suggest that statistical sparsity can lead to further performance benefits by imposing sparsity as a statistical prior on the considered signal. In this article, a comprehensive survey is made of recent progress on statistical sparsity based techniques for various radar imagery applications.MOE (Min. of Education, S’pore)Accepted versio
Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current...
This scientific article investigates the intricate relationship between two crucial aspects of radar...
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsit...
This chapter is concerned with the application of sparsity and compressed sensing ideas in imaging ...
Sparse signals are commonly expected in remote sensing and Earth observation. Along with the signifi...
This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR)...
Abstract—Recent developments in [1] and [2] introduced a novel regularization method for compressive...
Recent development of compressive sensing has greatly benefited radar imaging problems. In this pape...
The output of a radar front-end is typically a vast data stream which contains only a few parameters...
Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regulari...
Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressi...
Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressi...
This paper presents the application of Compressive Sensing (CS) theory in radar signal processing. C...
Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current...
International audienceIn the last few years, we witnessed to an extraordinary and still growing deve...
Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current...
This scientific article investigates the intricate relationship between two crucial aspects of radar...
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsit...
This chapter is concerned with the application of sparsity and compressed sensing ideas in imaging ...
Sparse signals are commonly expected in remote sensing and Earth observation. Along with the signifi...
This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR)...
Abstract—Recent developments in [1] and [2] introduced a novel regularization method for compressive...
Recent development of compressive sensing has greatly benefited radar imaging problems. In this pape...
The output of a radar front-end is typically a vast data stream which contains only a few parameters...
Recent developments in Carrillo et al. (2012) and Carrillo et al. (2013) introduced a novel regulari...
Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressi...
Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressi...
This paper presents the application of Compressive Sensing (CS) theory in radar signal processing. C...
Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current...
International audienceIn the last few years, we witnessed to an extraordinary and still growing deve...
Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current...
This scientific article investigates the intricate relationship between two crucial aspects of radar...
This paper presents a rigorous but tractable study of sparsity. We postulate a definition of sparsit...