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Multi-Sensor Sonobuoy

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-C-0021
Agency Tracking Number: N192-060-0524
Amount: $139,907.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N192-060
Solicitation Number: 19.2
Timeline
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-10-01
Award End Date (Contract End Date): 2020-04-03
Small Business Information
12900 Brookprinter Place, Suite 800
Poway, CA 92064
United States
DUNS: 107928806
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jeffrey Ridgway
 Principal Investigator/Project Manager
 (858) 373-2755
 jridgway@islinc.com
Business Contact
 Margaret Latchman-Geller
Phone: (858) 373-2717
Email: mgeller@islinc.com
Research Institution
N/A
Abstract

Acoustic quieting and high background noise have decreased the detection range of passive acoustics against threat submarines. Non-acoustic sensors can fill the gap and increase range, while maintaining covertness. ISL and its subcontractor USSI, will leverage extensive experience gained in designing, building and testing a two-sensor air-droppable A-size sonobuoy, into a successful multi-sensor sonobuoy (MSS) design that will improve air-ASW for tactical usage, intelligence, classification and tracking. In Phase 1, we will design a damped sonobuoy string that will minimize motion noise in acoustics, electric and magnetic field sensors. We will conduct tradeoff studies for extending the electric field aperture vs. reducing its SWAP, retaining directional capability and containing cost. We will integrate a scalar magnetic sensor into the MSS and minimize its noise via careful location and calibration/reduction algorithms. Adaptive algorithms that make use of colored clutter will be borrowed from radar processing to enhance the non-acoustic SNR, and we will create data fusion algorithms that effectively combine the three data types and optimize each sensor’s strengths. The increased detection ranges will benefit tactical scenarios, and the multiply sensed data types will improve classification, which will be a great benefit to the Navy in its ASW missions.

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

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