Adaptive Compressed Sensing for Mission Prioritized Data Collection and Analysis

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Department of Defense
Air Force
Award Year:
Phase I
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Small Business Information
111 Berkeley Drive, Syracuse, NY, 13210-
Hubzone Owned:
Minority Owned:
Woman Owned:
Principal Investigator:
Donald Leskiw
Director of Research
(315) 423-3985
Business Contact:
Donald Leskiw
(315) 423-3985
Research Institution:
Syracuse University
Patricia Lowney
Office of Sponsored Programs
113 Bowne Hall
Syracuse, NY, 13244-
(315) 443-2807
Nonprofit college or university
ABSTRACT: The test and evaluation (T & E) of complex systems is challenging in many respects. Vast amounts of data are typically generated, which need to be transmitted, stored, and analyzed. Traditionally, such testing has been"stove-piped,"with the data in one domain collected and analyzed independently of the others. Nowadays, however, T & E activities are being integrated. And dynamical as well as static testing is required when the temporal affects of disparate domains are not mutually independent. Accordingly, Leskiw Associates and the Data Fusion and the Aerospace Engineering research groups at Syracuse University are teaming together to develop Adaptive Compressive Sensing technologies for (multi-) Mission Prioritized Data Collection and Analysis. The Phase I domain of definition is wind tunnel data provided by the Aerospace Engineering group, and the baseline algorithm is one developed by the Data Fusion group. The immediate objective is a Proof-of-Principle demonstration of a new recursive compressive sensing approach for efficiently disseminating T & E data according to user"s needs. BENEFIT: The envisaged technology being developed here has many potential uses, besides its principal use for efficiently disseminating T & E data according to user"s needs. Leskiw Associates has identified two: distributed interferometric signal processing for Electronic Support (e.g., passively identifying radars); and multi-sensor fusion for phase-derived range aided tracking of ballistic objects, and discrimination between targets and decoys. And Syracuse University has identified several: the principal one is the University"s Skytop wind tunnel facility. Others include: inference functions for weak signals (e.g., detection, classification, parameter estimation); and netted sensor tracking of small objects; and sensor resource management.

* information listed above is at the time of submission.

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