Adaptive linear chirplet synchroextracting transform for time-frequency feature extraction of non-stationary signals

Time-frequency analysis methods is an effective tool to analyze non-stationary signals. Moreover, the utilization of postprocessing algorithms significantly enhances this analytical capability. However, these methods have certain limitations when dealing with non-stationary signals with strong time-varying laws. We put forward an adaptive linear chirplet synchroextracting transform (ALCSET) based on chirplet transform (CT) to deal with this problem. This paper first optimizes the CT by measuring Gini index to generate a time -frequency representation with accurate amplitude. Then, an improved synchroextracting operator is employed to obtain high-resolution and energy concentration time -frequency representation. The simulation experiments of nonstationary signals demonstrate that the significant advantages of the proposed method in terms of energy aggregation, noise robustness, and signal reconstruction. Furthermore, the practical value of the method is verified by the experimental signal.