Multiscale Chirplet Basis Sparse Signal Decomposition Method

This document introduces a highly effective approach called the Multiscale Chirplet Basis Sparse Signal Decomposition Method. This technique is particularly suitable for decomposing multicomponent non-stationary signals. The implementation employs an optimized matching pursuit algorithm that adaptively selects chirplet atoms from a multiscale dictionary to achieve sparse representations. Key functions include signal preprocessing, dictionary generation with variable time-frequency resolution, and iterative component extraction using orthogonal matching pursuit. Through this method, you can better understand signal constituents, enabling more accurate analysis and processing of complex signals. The complete MATLAB/Python source code is available, providing researchers with practical tools for implementing adaptive time-frequency analysis, signal separation, and feature extraction algorithms. We hope this information proves valuable for your signal processing applications.