Data Fusion of Electric Field and Acoustic Data

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$79,989.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
N68335-09-C-0258
Agency Tracking Number:
N091-019-1550
Solicitation Year:
2009
Solicitation Topic Code:
N091-019
Solicitation Number:
2009.1
Small Business Information
SeaLandAire Technologies, Inc.
1510 Springport Rd Suite C, Jackson, MI, 49202
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
005055582
Principal Investigator:
Dennis Byrne
Principal Electrical Engi
(517) 784-8340
dbyrne@sealandaire.com
Business Contact:
David Sparks
Principal Electrical Engi
(517) 784-8340
dsparks@sealandaire.com
Research Institution:
n/a
Abstract
Conventional acoustic ASW methods are limited in littoral regions, where multipath caused by widely varying sound speed profiles and cluttered boundary conditions is concurrent with high ambient noise. For this reason additional detection methods are desirable. Many potential targets of interest generate incidental electric fields from the galvanic potential field generated by dissimilar metals in contact with seawater. Ultimately, these low-frequency e-fields propagate through the water and can be detected at a moderate distance with e-field sensors; when combined with the acoustic data, the fused data can provide additional performance in terms of reduced false alarm rates, tracking, and classification. The opportunity, then, is to develop a tactically deployable, hybrid e-field and acoustic buoy that can support a multilayered data fusion approach to improve airborne ASW capability in cluttered littoral regions. This program will develop a conceptual buoy design that incorporates both the acoustic sensor and the e-field sensor for tactical surveillance and classification of marine vessels. The design will include in-buoy signal processing algorithms for data fusion of the e-field and acoustic sensors to increase probability of detection. System performance metrics will be predicted through simulation.

* information listed above is at the time of submission.

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