LAUSR

The model-based signal decomposition algorithm is an important research direction in the field of digital signal processing, especially based on the amplitude modulation and frequency modulation (AMFM) model. In this paper, a signal decomposition algorithm based on multiple complex AMFM model is proposed to analyze multi-model data sets. Firstly, the analyzed signal is converted into the form of the analytic signal because of the simple representation of the AMFM model in the analytic domain. Then, the multi-model optimization equation of the analytic signal is realized by the estimated instantaneous frequency (IF) of each model, which can be estimated by time–frequency analysis (TFA). Finally, each model parameter of the optimization equation is solved by the partial differential equation and the alternating direction method of multipliers method (ADMM) to find the global optimal solution of the signal. In the optimization equation, we introduce the leakage factor to improve the extraction accuracy of the model; at the same time, we employ the cyclic iteration method to optimize the equation parameters to improve the convergence rate of the algorithm. Several examples of the simulated and real-life signals are provided to show that the proposed algorithm can accurately estimate the parameters of each model in the signal.