Exploiting Agile Waveforms and Sampling for Compressive Sensing Radar

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$80,000.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
N00014-12-M-0057
Award Id:
n/a
Agency Tracking Number:
N112-161-1029
Solicitation Year:
2011
Solicitation Topic Code:
N112-161
Solicitation Number:
2011.2
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, -
Hubzone Owned:
N
Minority Owned:
Y
Woman Owned:
N
Duns:
859244204
Principal Investigator:
Les Novak
Principal Research Engine
(781) 933-5355
les.novak@ssci.com
Business Contact:
Jay Miselis
Corporate Controller
(781) 933-5355
contracts@ssci.com
Research Institute:
Stub




Abstract
Compressive Sensing (CS) has provided the radar community with a new mathematical framework for efficient and robust data collection and image formation. According to the theory of compressive sensing, a signal that is sparse in some domain can be recovered using far fewer samples than required by the Nyquist Sampling Theorem. Applications to radar were quick to emerge; the availability of high-resolution (traditional and synthetic aperture) radars that gather enormous amounts of data require faster, more efficient data processing algorithms to process the data. CS concepts have been applied to the radar imaging function; high range/velocity resolutions have been achieved using sufficiently smaller bandwidth than traditional radars. MIMO (multi-input, multi-output) radar has also provided the radar community with radar designs that achieve superior resolution compared to traditional systems having the same number of transmit and receive antennas. It is reasonable, therefore, to apply CS concepts to the design of MIMO radar systems. Since the direction of arrival (DOA) of targets approaching a radar system form a sparse vector in range-Doppler-angle space, compressive sensing concepts can be applied to the MIMO radar image formation and DOA estimation problems. SSCI is developing optimum radar transmit waveforms for CS/MIMO systems.

* information listed above is at the time of submission.

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