Compressed Sensing in On-Grid MIMO Radar
Author
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-30
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) radar.
Recent advances of Compressive Sensing offer a means of efficiently accomplishing this task.
The sparsity constraints needed to apply the techniques of Compressive Sensing to problems in radar systems have led to discretizations of the target scene in various domains, such as azimuth, time delay, and Doppler.
Building upon recent work, we investigate the feasibility of on-grid Compressive Sensing-based MIMO radar via a threefold azimuth-delay-Doppler discretization for target detection and parameter estimation.
We utilize a colocated random sensor array and transmit distinct linear chirps to a small scene with few, slowly moving targets.
Relying upon standard far-field and narrowband assumptions, we analyze the efficacy of various recovery algorithms in determining the parameters of the scene through numerical simulations, with particular focus on the l1-squared Nonnegative Regularization method.
American Psychological Association (APA)
Minner, Michael F.. 2015. Compressed Sensing in On-Grid MIMO Radar. The Scientific World Journal،Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1078735
Modern Language Association (MLA)
Minner, Michael F.. Compressed Sensing in On-Grid MIMO Radar. The Scientific World Journal No. 2015 (2015), pp.1-10.
https://search.emarefa.net/detail/BIM-1078735
American Medical Association (AMA)
Minner, Michael F.. Compressed Sensing in On-Grid MIMO Radar. The Scientific World Journal. 2015. Vol. 2015, no. 2015, pp.1-10.
https://search.emarefa.net/detail/BIM-1078735
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078735