Wavelet Transformation and Signal Discrimination for HRR Radar Target Recognition
Multidimensional Systems and Signal Processing
Neural Solutions for High Range Resolution Radar Classification
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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This 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.