Radar Waveform Feature Extraction Using Wavelet Energy Moments

Feature extraction for radar waveforms using wavelet energy moments. In radar signal processing, wavelet transform serves as a prevalent technique that decomposes signals into sub-signals at different frequencies, enabling the extraction of characteristics from diverse frequency components. Wavelet energy moments represent a widely adopted feature extraction approach, which computes the energy distribution of signals across various scales to characterize their spectral properties. By employing wavelet energy moments as features, we can more effectively analyze and interpret the characteristics of radar waveform signals. Implementation Approach: - Apply discrete wavelet transform (e.g., using MATLAB's wavedec function) to decompose radar signals into approximation and detail coefficients - Calculate energy values for each decomposition level by squaring and summing the coefficients (energy = sum(coeffs.^2)) - Compute energy moments by weighting energy values with corresponding scale indices - Normalize moments to create scale-invariant features for classification Key Functions: wavedec for decomposition, detcoef/appcoef for coefficient extraction, energy calculation via vector operations