Aircraft Target Recognition Using Support Vector Machine Classification on One-Dimensional Range Profiles

This implementation demonstrates aircraft target classification using Support Vector Machine (SVM) on one-dimensional High Resolution Range Profiles (HRRP). The program features detailed code structure including data preprocessing, feature extraction, SVM model training, and classification validation. Key components include HRRP normalization techniques, radial basis function (RBF) kernel implementation for non-linear classification, and cross-validation procedures for model optimization. The complete workflow covers signature preprocessing to remove noise artifacts, feature dimension optimization using principal component analysis (PCA), and multi-class classification handling through one-vs-rest strategy. This serves as an exemplary learning routine for implementing machine learning in radar-based target recognition systems, providing practical insights into SVM parameter tuning and performance evaluation metrics. The comprehensive documentation and modular code structure make it suitable for both educational purposes and practical application development.