High Fidelity Clutter Model for Active Sonar

Award Information
Agency:
Department of Defense
Amount:
$832,352.00
Program:
SBIR
Contract:
N00024-09-C-4156
Solitcitation Year:
2008
Solicitation Number:
2008.1
Branch:
Navy
Award Year:
2009
Phase:
Phase II
Agency Tracking Number:
N081-056-0538
Solicitation Topic Code:
N08-056
Small Business Information
Applied Physical Sciences Corp.
475 Bridge Street, Suite 100, Groton, CT, 06340
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Duns:
112716357
Principal Investigator
 Paul Koenigs
 Principal Scientist
 (860) 448-3253
 pkoenigs@aphysci.com
Business Contact
 David Horne
Title: Sr Vice President/CFO
Phone: (860) 448-3253
Email: dhorne@aphysci.com
Research Institution
N/A
Abstract
The results of the Phase I effort demonstrated the feasibility of constructing a realistic, high fidelity active sonar clutter model that is not computationally intensive. Our first objective is to create a library of clutter objects based on statistical descriptors extracted from existing sonar databases. Our second objective is to create several surrogate acoustic environments using range dependent propagation models and Navy accepted environmental models. The third objective is to design, build, test and evaluate a Training Clutter Model system for the SQQ-89A(V)15 On- Board-Trainer using a spiral development approach. The first build is a prototype capable of synthesizing the significant features of high fidelity clutter in a benign environment with a demonstration using an OBT or suitable alternative. The second build is a system prototype that includes complex environments and the flexibility to create environments and scenarios just prior to a training session. The final build is a system that demonstrates the ability of a training supervisor to inject high fidelity clutter into a typical training session. The fourth objective is to design and implement a Signal Processing Algorithm system based on the training model that is capable of stimulating advanced signal detection and classification algorithms.

* information listed above is at the time of submission.

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