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

Ivan Kadar; Chee-Yee Chong; Robert W. Schutz

doi:10.1117/12.2520529

16 May 2019 A framework for context change detection and management in probabilistic models for context in fusion

Ivan Kadar, Chee-Yee Chong, Robert W. Schutz

Proceedings Volume 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII; 110180P (2019) https://doi.org/10.1117/12.2520529
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Citation Download Citation

Ivan Kadar, Chee-Yee Chong, and Robert W. Schutz "A framework for context change detection and management in probabilistic models for context in fusion", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180P (16 May 2019); https://doi.org/10.1117/12.2520529

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