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Subspace Tracking and Manifold Learning Based Heterogeneous Data Fusion for Unexpected Event Discovery

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-16-C-0243
Agency Tracking Number: F16A-T12-0051
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF16-AT12
Solicitation Number: 2016.0
Solicitation Year: 2016
Award Year: 2016
Award Start Date (Proposal Award Date): 2016-08-02
Award End Date (Contract End Date): 2017-04-20
Small Business Information
20271 Goldenrod Lane
Germantown, MD 20876
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Genshe Chen
 (240) 481-5397
Business Contact
 Yingli Wu
Phone: (301) 515-7261
Research Institution
 Virginia Commonwealth University
 Ruixin Niu
Department of Electrical and Computer Engineering 601 West Main Street
Richmond, VA 23284
United States

 (804) 828-0030
 Nonprofit College or University

We aim to develop data-driven heterogeneous data fusion approaches for unanticipated event/target detection, which will be more robust and immune to model mismatch problems encountered by model-based approaches. Considering the low intrinsic dimensionality of the sensor data, we propose several data-level fusion approaches based on some state-of-the-art dimensionality reduction techniques. For linear sensor measurements, we propose two efficient joint linear subspace tracking approaches. The first joint subspace tracking approach is based on the concept of joint sparsity, compressive sensing techniques, and grid computation, which is suitable when the correlations between data from different sensing modalities are unclear. The second approach is based on the emerging compressive covariance sensing technique, which provides a faster and more accurate solution when explicit models are available for correlations between heterogeneous data streams. For nonlinear data, we propose a joint nonlinear manifold learning based data fusion framework, in which the nonlinear mapping from the target parameters to the measurements is learned and the classifier is trained with heterogeneous data. Computationally efficient online joint nonlinear manifold learning approaches will also be developed for unexpected event/target discovery. The proposed research will be conducted under the guidance of rigorous mathematical/statistical principles.

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

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