Advanced feature selection methodology for automatic target recognition

The paper investigates independent feature selection as used in neural networks for solving classification problems. Radial basis functions and wavelet transforms are used to preprocess the input data. A class of nonorthogonal classifiers is defined and their properties are investigated. It is demonstrated that nonorthogonal classifiers perform better than the orthogonal ones. Feature selection using mutual information is also investigated. Independence of features based on the information content is defined and used to select features for synthesis of ontogenic neural networks. Simulation results using synthetically generated radar returns showed promise for automatic target recognition