Aircraft Target Classification using Support Vector Machines for One-Dimensional Range Profiles

Support Vector Machine (SVM) is a powerful supervised learning algorithm widely used for classification tasks. In radar target recognition, High-Resolution Range Profiles (HRRP) serve as effective representations of target scattering characteristics and are commonly employed for aircraft target classification and identification.

Fundamental Principles HRRP represents the distribution of target scattering intensity along the radar range dimension, reflecting the target's geometric structure and material properties. SVM achieves HRRP data classification by identifying optimal hyperplanes that maximize the classification margin between different categories. In code implementation, this typically involves using scikit-learn's SVC class or MATLAB's fitcsvm function with proper kernel selection.

Implementation Approach Data Preprocessing: Apply normalization and noise reduction techniques to raw HRRP data to minimize noise and amplitude fluctuation effects. Code implementation typically includes z-score normalization and wavelet denoising filters. Feature Extraction: Select statistical features (mean, variance), frequency-domain features (Fourier transform coefficients), or time-frequency analysis features to enhance classification performance. Python implementations often use scikit-learn's Feature Extraction modules or custom feature calculation functions. SVM Training: Choose appropriate kernel functions (such as Gaussian RBF kernel or linear kernel) and optimize key parameters (like penalty factor C and kernel parameter γ) through cross-validation. Implementation commonly utilizes GridSearchCV for parameter optimization. Classification Evaluation: Assess model performance using metrics like confusion matrices, accuracy, recall rate, and F1-score through scikit-learn's classification_report function.

Extended Applications Combine with Principal Component Analysis (PCA) for feature dimension reduction to improve computational efficiency, implemented using decomposition.PCA in scikit-learn. Employ ensemble learning methods (such as Bagging or Boosting) to further enhance recognition rates, potentially using AdaBoost or Random Forest classifiers.

This implementation serves as a standard routine for radar target recognition and can be adapted to other similar one-dimensional signal classification problems with appropriate parameter tuning and feature engineering.