Air Target Classification Using High-Resolution Range Profiles and Deep Machine Learning Techniques

Air target classification using High-Resolution Range Profiles (HRRP) has become an important research area in modern radar signal processing and intelligent surveillance systems. This project presents an efficient air target classification framework using machine learning and deep learning techniques for accurate identification of aerial objects based on HRRP data. The proposed system is developed using the Flask web framework with integrated database management and user authentication for secure access and data handling. Initially, the uploaded HRRP dataset is preprocessed and analyzed to extract meaningful feature representations. Multiple classification algorithms including Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) are implemented and evaluated for performance comparison. The system computes important evaluation metrics such as accuracy, precision, recall, and F1-score to determine the most effective model for classification. Experimental results demonstrate that deep learning-based models, particularly MLP and CNN, achieve superior classification accuracy compared to traditional machine learning approaches. The application also provides an interactive prediction module that accepts user inputs and generates classification results with confidence scores and recommendations. The proposed framework offers high reliability, scalability, and efficient decision-making capability for intelligent radar-based air target identification systems. This work highlights the significance of combining HRRP analysis with advanced deep learning techniques for improved aerospace surveillance and defense applications.

Keywords — High-Resolution Range Profile (HRRP), Air Target Classification, Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), XGBoost, Radar Signal Processing, Flask Framework, Predictive Analytics.