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An approach based on multi-scale chirplet sparse signal decomposition is proposed to separate the multi-component polynomial phase signals,and estimate their instantaneous frequencies.In this paper,we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time interval.At every decomposition stage,we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polynomial phase signal component whose correlation with the current residue signal is largest.Simultaneously,the polynomial instantaneous frequency is estimated by connecting the linear frequency of the chirplet functions adopted in the current separation.Simulation experiment demonstrated that this method can separate the components of the multi-component polynomial phase signals effectively even in the low signal-to-noise ratio condition,and estimate its instantaneous frequency accurately.
An approach based on multi-scale chirplet sparse signal decomposition is proposed to separate the multi-component polynomial phase signals, and estimate their instantaneous frequency.In. Paper, we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time interval. At every decomposition stage, we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polynomial phase signal component whose correlation with the current residue signal is largest. Simultaneously, the polynomial instantaneous frequency is estimated by connecting the linear frequency of the chirplet functions adopted in the current separation. Simulation of this method can separate the components of the multi-component polynomial phase of the signal even in the low signal-to-noise ratio condition, and esti mate its instantaneous frequency accurately.