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 non-stationary 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.

中文翻译：

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用于非平稳信号时频特征提取的自适应线性线性调频同步提取变换


时频分析方法是分析非平稳信号的有效工具。此外，后处理算法的使用显着增强了这种分析能力。但这些方法在处理时变规律较强的非平稳信号时都存在一定的局限性。我们提出了一种基于线性调频变换（CT）的自适应线性调频同步提取变换（ALCSET）来解决这个问题。本文首先通过测量基尼指数来优化 CT，以生成具有精确幅度的时频表示。然后，采用改进的同步提取算子来获得高分辨率和能量集中的时频表示。非平稳信号的仿真实验表明，该方法在能量聚合、噪声鲁棒性和信号重构方面具有显着优势。此外，实验信号验证了该方法的实用价值。