PROCEEDINGS ARTICLE | May 22, 2015
Proc. SPIE. 9498, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015
KEYWORDS: Cognitive modeling, Detection and tracking algorithms, Data modeling, Visualization, Cameras, Sensors, Cognition, Algorithm development, Sensor fusion, Data fusion
We are interested in data fusion strategies for Intelligence, Surveillance, and Reconnaissance (ISR) missions. Advances
in theory, algorithms, and computational power have made it possible to extract rich semantic information from a wide
variety of sensors, but these advances have raised new challenges in fusing the data. For example, in developing fusion
algorithms for moving target identification (MTI) applications, what is the best way to combine image data having
different temporal frequencies, and how should we introduce contextual information acquired from monitoring cell
phones or from human intelligence? In addressing these questions we have found that existing data fusion models do not
readily facilitate comparison of fusion algorithms performing such complex information extraction, so we developed a
new model that does. Here, we present the Spatial, Temporal, Algorithm, and Cognition (STAC) model. STAC allows
for describing the progression of multi-sensor raw data through increasing levels of abstraction, and provides a way to
easily compare fusion strategies. It provides for unambiguous description of how multi-sensor data are combined, the
computational algorithms being used, and how scene understanding is ultimately achieved. In this paper, we describe
and illustrate the STAC model, and compare it to other existing models.
