论文标题
利用深度卷积神经网络的无跨度时间频率表示
Crossterm-Free Time-Frequency Representation Exploiting Deep Convolutional Neural Network
论文作者
论文摘要
双线性时频表示（TFRS）提供了非组织信号的高分辨率时变频率特征。但是，由于双线性性质，它们都遭受了跨境的困扰。现有的交叉降低的TFR专注于优化的内核设计，相当于歧义功能域中的低通量或掩盖。固定和自适应核的优化很困难，尤其是对于复杂的信号，它们的自动功能和跨标志在歧义函数中重叠。在这封信中，我们开发了一种新方法，可以有效地抑制具有Crossterms的非组织信号的高分辨率TFR。所提出的方法利用了深层卷积神经网络，该网络经过训练以构建无交叉TFR。通过模拟结果验证了所提出的方法的有效性，这些结果清楚地表明了理想的自动保存和跨境缓解功能。提出的技术明显优于基于自适应核和压缩感测技术的最先进的时频分析算法。
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.
