Sensor Fusion: Deriving Information from Data
The current explosion in the variety, capability and sheer number of sensors is generating a flood of data intended to enhance decision-making and human-system performance. However, this vast amount of data can easily lead to information overload and obscure the most relevant aspects of a situation. This is a critical problem for many users, most notably war fighters, who need to make rapid and correct tactical decisions. Increasingly, sensor fusion algorithms are merging and analyzing geospatial data to help extract actionable intelligence.
Sensor fusion can be accomplished in several ways. According to Alina Zare, an assistant professor in the Electrical & Computer Engineering Department at the University of Missouri, sensor fusion can include:
- co-registering and overlaying sensor outputs
- fusing raw sensor data
- extracting features/key points of interest from each sensor independently and then fusing the feature sets
- applying independent algorithms to each sensor (e.g., a target detection algorithm on each sensor individually) and then fusing the algorithm (e.g., detection) results.