论文标题
用于高分辨率时间分布的内核学习
Kernel Learning for High-Resolution Time-Frequency Distribution
论文作者
论文摘要
高分辨率和跨学期（CT）自由时频分布（TFD）的设计一直是一个开放的问题。基于经典内核的方法也受到TFD分辨率和CT抑制之间的权衡，即使在最佳派生参数下也是如此。为了打破当前的限制，我们直接基于Wigner-Ville分布（WVD）直接提出了一个数据驱动的内核学习模型。提出的基于内核学习的TFD（KL-TFD）模型包括几个堆叠的多通道学习卷积内核。具体而言，使用跳过操作员来维持正确的信息传输，并采用加权块来利用空间和频道依赖性。这两种设计同时实现了高的TFD分辨率和CT消除。关于合成和现实世界数据的数值实验证实了所提出的KL-TFD优于传统内核函数方法的优势。
The design of high-resolution and cross-term (CT) free time-frequency distributions (TFDs) has been an open problem. Classical kernel based methods are limited by the trade-off between TFD resolution and CT suppression, even under optimally derived parameters. To break the current limitation, we propose a data-driven kernel learning model directly based on Wigner-Ville distribution (WVD). The proposed kernel learning based TFD (KL-TFD) model includes several stacked multi-channel learning convolutional kernels. Specifically, a skipping operator is utilized to maintain correct information transmission, and a weighted block is employed to exploit spatial and channel dependencies. These two designs simultaneously achieve high TFD resolution and CT elimination. Numerical experiments on both synthetic and real-world data confirm the superiority of the proposed KL-TFD over traditional kernel function methods.
