Maximum Matching Initial Selection for Adaptive Gaussian Chirplet Decomposition

Adaptive Gaussian Chirplet Decomposition (AGCD) is a time-frequency signal decomposition algo -rithm with high resolution. The Gaussian chirplet basis adopted has variable time width, frequency center with linear chirp, which has both good time and frequency energy localization. But this basis is not orthogonal, and the computation in searching basises when decomposing a signal is very huge. AGCD can reduce computation by convert the optimization process to a traditional curve-fitting problem. But the performance of the AGCD is highly dependent on the initial selection. Traditional energy based initial selection fails in some cases when two or more basis has deep cross. The proposed maximum matching based initial selection is a fast and accurate ba-sis searching algorithm, which choose the best correlated basis each time within several candidates. Simulation results show that the new algorithm is much more stable and accurate than the energy based one without incre -asing computation.