Fuzzy classification algorithm as applied to signal discrimination for Navy theater wide missile defense

Given a set of training data and a feature extraction teal, fuzzy membership functions are created using regression analysis on the extracted features. These membership functions are then used to classify a signal into one of two basic classes (namely, 'threat' or 'non-threat'). Alternately, the data can be classified into M groups, as desired. For this paper, the training data form a set of modeled infrared intensities for subpixel objects, of the types expected for a prototypical ballistic missile defense engagement scenario. The feature extraction tool used is a form of local discriminant bases, as described by Coifman and Saito.(4) The top N features (typically two to four) are then piped pairwise through a regression tool to determine if any statistically significant trends occur. If a trend is discovered, then a membership function is created for the relationship; otherwise, membership functions are created for each feature independently. An example of each is given. Results indicate great flexibility in managing misclassification of targets (Leakage) versus classifying a non-target as a target (False Alarms), depending on the choice of membership functions. Results for using seven extracted features on performance data show < 1% Leakage corresponding to 13% False Alarms.