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Feature set evaluation for a generic missile detection system

Avan, K. Selçuk
Missile Detection System (MDS) is one of the main components of a self-protection system developed against the threat of guided missiles for airborne platforms. The requirements such as time critical operation and high accuracy in classification performance make the ‘Pattern Recognition’ problem of an MDS a hard task. Problem can be defined in two main parts such as ‘Feature Set Evaluation’ (FSE) and ‘Classifier’ designs. The main goal of feature set evaluation is to employ a dimensionality reduction process for the input data set, while not disturbing the classification performance in the result. In this thesis study, FSE approaches are investigated for the pattern recognition problem of a generic MDS. First, synthetic data generation is carried out in software environment by employing generic models and assumptions in order to reflect the nature of a realistic problem environment. Then, data sets are evaluated in order to draw a baseline for further feature set evaluation approaches. Further, a theoretical background including the concepts of Class Separability, Feature Selection and Feature Extraction is given. Several widely used methods are assessed in terms of convenience for the problem by giving necessary justifications depending on the data set characteristics. Upon this background, software implementations are performed regarding several feature set evaluation techniques. Simulations are carried out in order to process dimensionality reduction. For the evaluation of the resulting data sets in terms of classification performance, software implementation of a classifier is realized. Resulting classification performances of the applied approaches are compared and evaluated.