A variety of approaches exist for combining data from multiple sensors. The model-based approach combines data based on its support for or refutation of elements of the model which in turn can be used to evaluate an experimental thesis. This paper presents a collection of algorithms for mapping various types of sensor data onto a thesis-based model and evaluating the truth or falsity of the thesis, based on the model. The use of this approach for autonomously arriving at findings and for prioritizing data are considered. Techniques for updating the model (instead of arriving at a true/false assertion) are also discussed.

Date Published: 20 June 2014
PDF: 9 pages
Proc. SPIE 9091, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII, 90911Q (20 June 2014); doi: 10.1117/12.2049501

Published in SPIE Proceedings Vol. 9091:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII
Ivan Kadar, Editor(s)