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Optimization based expanded dictionary adaptive antenna array modeling for far field low frequency propagation

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911NF-19-P-0031
Agency Tracking Number: A18B-009-0183
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A18B-T009
Solicitation Number: 18.B
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2018-12-17
Award End Date (Contract End Date): 2019-06-17
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Sergey Voronin
 Senior Scientist
 (301) 294-5271
Business Contact
 Mark James
Phone: (301) 294-5200
Research Institution
 The Ohio State University
 Fernando Lisboa Teixeira Fernando Lisboa Teixeira
Office of Sponsored Programs, 1960 Kenny Rd.
Columbus, OH 43210
United States

 (614) 292-6993
 Nonprofit College or University

Leveraging existing multi-frequency antenna arrays for purposes they were not originally designed would greatly improve the benefits of such systems. For example, the use of arrays of small antennas with novel waveforms may produce low frequency signals in the far field that are not possible using single antennas and classical waveforms. In particular, we propose to investigate the formation of low frequency signals using a combination of several small wide-band antennas, radiating localized signal forms. We seek to represent an arbitrary low frequency wave signal as a linear combination of localized Hermite functions and compactly supported Wavelet basis functions from several families. We will investigate the design of wavelets with optimal shifts, dilations and amplitudes. The use of a dictionary of different basis functions will allow us to represent arbitrary sinusoids accurately with a small amount of antennas. In order to pick the optimal weights in the linear combination with compactly supported functions, we will investigate the use of iteratively reweighted least squares and moving average approaches. Antenna array weights will be optimized per desired constraints such as maximal signal to noise ratio and minimal variance. Applications of our designs to time reversal detection will be demonstrated.

* Information listed above is at the time of submission. *

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