Complete MATLAB Implementation for DS-UWB Signal Processing

This complete MATLAB program handles DS-UWB signal processing through a systematic workflow. The implementation begins with raw data acquisition using MATLAB's file I/O functions (e.g., fread or load) followed by preprocessing steps including noise filtering through digital filters (Butterworth/Chebyshev) and signal normalization. Feature extraction leverages time-domain and frequency-domain analysis techniques, employing functions like pwelch for power spectral density estimation and statistical feature calculation. The program incorporates feature selection algorithms such as sequential feature selection or principal component analysis (PCA) to identify optimal feature subsets. Machine learning model training utilizes MATLAB's Classification Learner app or programming interfaces (fitcsvm, fitctree) with cross-validation techniques. Prediction modules apply trained models to new datasets using predict functions, while evaluation metrics including confusion matrices and ROC curves provide performance analysis through dedicated visualization tools. The final report generation employs MATLAB's reporting capabilities to document processing parameters and results systematically. This implementation facilitates comprehensive DS-UWB data analysis, supporting subsequent research and practical applications through its modular, well-documented code structure.