Auxiliary System Sensor Fusion

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-13-C-0118
Agency Tracking Number: N112-159-0259
Amount: $486,301.00
Phase: Phase II
Program: SBIR
Awards Year: 2013
Solitcitation Year: 2011
Solitcitation Topic Code: N112-159
Solitcitation Number: 2011.2
Small Business Information
Technical Documentation Inc
1150 First Avenue, Suite 610, King of Prussia, PA, -
Duns: 198602120
Hubzone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Rich Kiefer
 Principal Investigator
 (321) 254-6970
 rkiefer@metrolink.net
Business Contact
 Dean Mancini
Title: President
Phone: (215) 957-1650
Email: dean.mancini@tdicorporation.net
Research Institution
 Stub
Abstract
The objective of ONR SBIR Solicitation Topic N112-159, Auxiliary System Sensor Fusion, is to develop methods and algorithms that allow sensor information from disparate auxiliary systems to be intelligently fused to provide enhanced situational awareness. A Phase II proposal in response to the solicitation topic has been jointly prepared by Technical Documentation Incorporated (TDI), a wholly-owned small business based in King of Prussia, PA and the Center for Data Analytics and Biomedical Informatics, Computer and Information Sciences Department, Temple University based in Philadelphia, PA. The 18-month base period and the nine-month option period contract will involve continuation of the Phase I activity, including use of software, data and documentation from the Government, which defines the notional system simulation of a reduced scale hardware implementation of a shipboard chilled water system and an electrical system. This software, data and documentation, in conjunction with the MATLAB, Simulink and Toolboxes kits software tool license purchased by TDI, is being used to build intelligent algorithms, for the fusion of data obtained from the simulated remote sensors. The intelligent algorithms are based on proven techniques such as Bayesian belief networks, linear and nonlinear classifiers, Kalman filtering, and Dempster-Schafer. Other techniques may be investigated as they are identified and time and material resources permit.

* information listed above is at the time of submission.

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