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Multi-Modal Sensor Fusion Utilizing Statistical Dependence and Compressive Sampling

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-15-C-0217
Agency Tracking Number: F15A-T16-0061
Amount: $149,999.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF15-AT16
Solicitation Number: 2015.1
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-07-30
Award End Date (Contract End Date): 2016-04-29
Small Business Information
Beeches Professional Campus 7980 Turin Road, Bldg. 1
Rome, NY 13440
United States
DUNS: 883336190
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Richard Loe
 Senior Research Scientist
 (315) 334-1163
Business Contact
 Thomas Benjamin
Phone: (315) 334-1163
Research Institution
 Syracuse University
 Dr. Pramod Varshney
Office of Sponsored Programs 113 Bowne Hall
Syracuse, NY 13244-1200
United States

 (315) 443-1060
 Domestic Nonprofit Research Organization

ABSTRACT: Our goal is to develop theoretical frameworks for efficient multisensor fusion of high dimensional data for target detection localization and tracking. We plan to develop novel algorithms based upon our previous research on copula theory to perform inference with multi-modal correlated sensor data. Copulas describes the dependence between random variables and allow one to optimally exploit the inherent low-dimensional characteristics of high dimensional data. They are popular in high-dimensional statistical applications as they allow one to easily model and estimate the distribution of random vectors by estimating marginals and copulae separately. There are many parametric copula families available that capture more information than traditional approaches while still maintaining low data communication rates to the central fusion center. We will evaluate achievable performance limits of the developed algorithms and compare the efficiency to several benchmark algorithms. Testing will be performed on physics based simulated data sets. The benchmark techniques we will consider will include conventional fusion of Kalman state spaces, principal component analysis (PCA) based methods and parametric likelihood-based approaches. The research output is expected to have significant implications in coping with the data deluge problem at individual sensors employed for detection, estimation and tracking in sensor and radar networks. ; BENEFIT: The proposed approach will permit improved sensor fusion of heterogeneous data sets with only a small increase in communication requirements over traditional fusion approaches. The technology benefits Air Force ISR and missile defense systems. Commercial applications include aviation radar systems as well as emerging multisensor systems.

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

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