Abstract Signal decomposition has drawn growing interest in various applications these days. Some recent decomposition methods, like the variational mode decomposition (VMD) and the variational nonlinear chirp mode decomposition (VNCMD), employ a joint-optimization scheme to accurately estimate all the signal modes underlying a signal. Some existing issues for these methods are: requiring prior knowledge of the number of the signal modes, empirically setting the bandwidth parameter to a fixed value, and lacking of an effective initialization scheme for the optimization algorithm. To address these issues, this paper presents a new decomposition approach called adaptive chirp mode pursuit (ACMP). Similar to the matching pursuit method, the ACMP captures signal modes one by one in a recursive framework. In addition, an adaptive bandwidth parameter updating rule and an instantaneous frequency initialization method based on Hilbert transform are incorporated into the ACMP. Several examples including simulated signals as well as real-life applications are provided to show the effectiveness and advantages of the ACMP.

中文翻译：

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自适应啁啾模式追踪：算法与应用

摘要 如今，信号分解在各种应用中引起了越来越大的兴趣。最近的一些分解方法，如变分模式分解 (VMD) 和变分非线性啁啾模式分解 (VNCMD)，采用联合优化方案来准确估计信号下的所有信号模式。这些方法存在的一些问题是：需要先了解信号模式的数量，凭经验将带宽参数设置为固定值，以及缺乏有效的优化算法初始化方案。为了解决这些问题，本文提出了一种称为自适应啁啾模式追踪 (ACMP) 的新分解方法。与匹配追踪方法类似，ACMP 在递归框架中逐一捕获信号模式。此外，自适应带宽参数更新规则和基于希尔伯特变换的瞬时频率初始化方法被纳入ACMP。提供了几个示例，包括模拟信号以及实际应用，以展示 ACMP 的有效性和优势。