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IR/RF SPARK - IR/RF fusion using Stochastic Programming And Robust Kinematic features

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: HQ0147-13-C-7189
Agency Tracking Number: B12A-002-0036
Amount: $99,999.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: MDA12-T002
Solicitation Number: 2012.A
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2012-10-24
Award End Date (Contract End Date): 2013-05-31
Small Business Information
500 West Cummings Park - Ste 3000
Woburn, MA -
United States
DUNS: 859244204
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Ranga Narayanaswami
 Sr Grp Leader: Signal Exploitation
 (781) 933-5355
Business Contact
 Jay Miselis
Title: Corporate Controller
Phone: (781) 933-5355
Research Institution
 Josh Tenebaum
77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States

 (617) 452-2010
 Nonprofit College or University

SSCI and MIT team will approach a problem of fusing target data from sensor of different phenomenology using Probabilistic programming technology, Stochastic inference techniques based on Markov chain simulation, and Robust kinematic features. These methods will allow us to estimate the extend of information on metric, material, and kinematic properties of the observed low resolution targets available through infra-red and radar sensors. Any collaborating information available through both sensors would help their association and tracking between sensors of different phenomenology. Stochastic inference techniques based on Markov chain simulation will be employed to rapidly identify accurate interpretations of the data. Probabilistic programs go beyond classical pattern recognition techniques, statistical learning methods and Bayesian networks. These programs simulate hypothetical worlds according to the prior assumptions. Each possible execution path of the probabilistic program constitutes a distinct hypothesis over which Bayesian inference can be performed. In this framework, it becomes natural to capture detailed physical prior knowledge, even if the distributions involved are highly non-Gaussian and their time evolution is highly non-linear, making analytical representations intractable. The programs will also help the team assess the best wavebands to provide the richest set of potentially useful features for target characterization.

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

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