FTI’s Dynamically Adaptive Sensor Data Fusion (DASDAF) Technology Supports Any Pattern Recognition Application
Pattern recognition is the process of classifying data based either on a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified consist of groups of measurements or observations, defining points in a to be classified or described, a feature extraction mechanism that computes numeric or symbolic information from the observations, and a classification methodology that describes the observations, relying on the extracted features.
DASDAF is a multi-sensor state-of-the-art pattern recognition paradigm that combines sensor data and contextual information to form an accurate picture from the available data. It provides the ability to:
Exploit prior & learned knowledge of pattern attributes
Store descriptive parameters in a dynamic database
Adapt to varied collection conditions and changing parameters
Refine initial classification to produce final pattern matches
Fully disclose why each pattern match occurs and why other patterns don’t match
Dynamic Adaptive Sensor Data Fusion Features
Dynamic Adaptive Sensor Data Fusion Examples | 
