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A framework for context change detection and management in probabilistic models for context in fusion
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Paper Abstract

In a prior paper, a probabilistic model for using context in fusion was developed. It was shown that context-based fusion could be represented by a Bayesian probabilistic model that contains situation and context data, as well as conditional probabilities for the random variables. In the same paper, a conceptual model of an adaptive real-time context management system was proposed to monitor fusion performance, and select the appropriate context in order to improve fusion performance. This paper represents an extension of the above paper by developing frameworks for an adaptive general real-time context management, with application to optimize the tracking performance of an airborne platform.

Paper Details

Date Published: 16 May 2019
PDF: 11 pages
Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180P (16 May 2019); doi: 10.1117/12.2520529
Show Author Affiliations
Ivan Kadar, Interlink Systems Sciences, Inc. (United States)
Chee-Yee Chong, Independent Consultant (United States)
Robert W. Schutz, Independent Consultant (United States)


Published in SPIE Proceedings Vol. 11018:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII
Ivan Kadar; Erik P. Blasch; Lynne L. Grewe, Editor(s)

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