Add to ORKGWe consider a multiple-input-multiple-output radar system and derive a theoretical framework for the recoverability of targets in the azimuth-range domain and the azimuth-range-Doppler domain via sparse approximation algorithms. Using tools developed in the area of compressive sensing, we prove bounds on the number of detectable targets and the achievable resolution in the presence of additive noise. Our theoretical findings are validated by numerical simulations
Multiple input multiple output (MIMO) radar forms large virtual aperture and improves the cross-rang...
A sparse recovery based transmit-receive angle imaging scheme is proposed for bistatic multiple-inpu...
The dissertation discusses compressive sensing and its applications to localization in multiple-inpu...
Compressed sensing techniques make it possible to exploit the sparseness of radar scenes to potentia...
In sparse sensing based distributed MIMO radars, the problem of target estimation is formulated as a...
Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar...
Abstract—Compressed sensing is a technique for efficiently sampling signals which are sparse in some...
Abstract—We study compressive sensing in the spatial domain to achieve target localization, specific...
The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) ...
Abstract—Sparse sensing-based distributed MIMO radars ex-ploit the sparsity of the targets in the di...
Abstract—This paper considers range-angle-Doppler estima-tion in collocated, compressive sensing-bas...
• We consider sparse sensing-based distributed MIMO radars, which exploit the sparsity of the target...
Multi-input and multi-output (MIMO) radars achieve high resolution of arrival direction by transmitt...
Abstract — We consider the problem of target estimation in distributed MIMO radars that employ compr...
By exploring sparsity in the target space, compressive sensing (CS) based multi-input multi-output (...
Multiple input multiple output (MIMO) radar forms large virtual aperture and improves the cross-rang...
A sparse recovery based transmit-receive angle imaging scheme is proposed for bistatic multiple-inpu...
The dissertation discusses compressive sensing and its applications to localization in multiple-inpu...
Compressed sensing techniques make it possible to exploit the sparseness of radar scenes to potentia...
In sparse sensing based distributed MIMO radars, the problem of target estimation is formulated as a...
Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar...
Abstract—Compressed sensing is a technique for efficiently sampling signals which are sparse in some...
Abstract—We study compressive sensing in the spatial domain to achieve target localization, specific...
The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) ...
Abstract—Sparse sensing-based distributed MIMO radars ex-ploit the sparsity of the targets in the di...
Abstract—This paper considers range-angle-Doppler estima-tion in collocated, compressive sensing-bas...
• We consider sparse sensing-based distributed MIMO radars, which exploit the sparsity of the target...
Multi-input and multi-output (MIMO) radars achieve high resolution of arrival direction by transmitt...
Abstract — We consider the problem of target estimation in distributed MIMO radars that employ compr...
By exploring sparsity in the target space, compressive sensing (CS) based multi-input multi-output (...
Multiple input multiple output (MIMO) radar forms large virtual aperture and improves the cross-rang...
A sparse recovery based transmit-receive angle imaging scheme is proposed for bistatic multiple-inpu...
The dissertation discusses compressive sensing and its applications to localization in multiple-inpu...