论文标题
使用信号创新的稀疏建模对多组分信号的时频脊估计
Time-Frequency Ridge Estimation of Multi-Component Signals using Sparse Modeling of Signal Innovation
论文作者
论文摘要
本文提出了一种新的方法，用于估计观察到的非平稳混合信号的模式。首先在短时傅立叶变换和稀疏采样理论之间建立了一个链接，其中观测值被建模为通过已知函数过滤的脉冲流。由于要检索的信号具有有限的创新速率（FRI），因此使用改编的重建方法来估计存在噪声的信号模式。我们将结果与最先进的方法进行比较，并通过突出不同情况下的估计性能来验证我们的方法。我们的方法铺平了基于未来的星期五模式解开算法的方式。
This paper presents a novel approach for estimating the modes of an observed non-stationary mixture signal. A link is first established between the short-time Fourier transform and the sparse sampling theory, where the observations are modeled as a stream of pulses filtered by a known function. As the signal to retrieve has a finite rate of innovation (FRI), an adapted reconstruction approach is used to estimate the signal modes in the presence of noise. We compare our results with state-of-the-art methods and validate our approach by highlighting an improvement of the estimation performance in different scenarios. Our approach paves the way of future FRI-based mode disentangling algorithms.
