利用深度卷积神经网络的无串扰时频表示
Crossterm-Free Time-Frequency Representation Exploiting Deep Convolutional Neural Network
摘要：
双线性时频表示（TFR）提供了非平稳信号的高分辨率时变频率特性。然而，由于双线性性质，它们遭受交叉项。现有的交叉项减少的TFR集中在优化的内核设计，这相当于低通加权或屏蔽在模糊函数域。固定和自适应核的优化是困难的，特别是对于其自项和交叉项在模糊函数中重叠的复杂信号。在这封信中，我们开发了一种新的方法，提供高分辨率的非平稳信号的TFRs与交叉项有效抑制。所提出的方法利用了一个深度卷积神经网络，该网络经过训练以构建无交叉项的TFR。所提出的方法的有效性进行了验证，仿真结果清楚地显示出理想的自项保存和跨项缓解能力。所提出的技术显着优于国家的最先进的时频分析算法的基础上自适应内核和压缩感知技术。
英文摘要：
Bilinear time-frequency representations (TFRs) provide high-resolution time-varying frequency characteristics of nonstationary signals. However, they suffer from crossterms due to the bilinear nature. Existing crossterm-reduced TFRs focus on optimized kernel design which amounts to low-pass weighting or masking in the ambiguity function domain. Optimization of fixed and adaptive kernels are difficult, particularly for complicated signals whose autoterms and crossterms overlap in the ambiguity function. In this letter, we develop a new method to offer high-resolution TFRs of nonstationary signals with crossterms effectively suppressed. The proposed method exploits a deep convolutional neural network which is trained to construct crossterm-free TFRs. The effectiveness of the proposed method is verified by simulation results which clearly show desirable autoterm preservation and crossterm mitigation capabilities. The proposed technique significantly outperforms state-of-the-art time-frequency analysis algorithms based on adaptive kernels and compressive sensing techniques.
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