论文标题
增强的时频表示和模式分解
Enhanced Time-Frequency Representation and Mode Decomposition
论文作者
论文摘要
允许进行模式重建的时频表示（TFR）在解释和分析由各种模式构成的非平稳信号中起着重要作用。但是，大多数以前的方法很难处理具有近距离或频谱的瞬时频率（IFS），尤其是在不利环境中的信号模式。为了解决这个问题，我们提出了一个增强的TFR和模式分解（ETFR-MD）方法，该方法特别适合代表和分解在低信噪比（SNR）条件下具有关闭或越过IFS的多模式信号。根据短时傅立叶变换（STFT），将提出的ETFR-MD的重点放在每个信号模式的准确性和瞬时振幅（IA）上。首先，我们设计了一种专门针对涉及越过IF的案例的初始IF估计方法。此外，提出了一个低复杂模式增强方案，以增强如果法律，则可以更好地适合基础。最后，从信号的STFT系数中提取的IA提取与增强的IFS相结合，使我们能够重建每个信号模式。此外，我们得出数学表达式，这些表达式揭示了我们方法的最佳窗口长度和分离干扰。所提出的ETFR-MD与以前的相关方法兼容，因此可以将其视为迈向更一般的时频表示和分解方法的一步。与最先进的基准相比，实验结果证实了ETFR-MD的出色性能。
Time-frequency representation (TFR) allowing for mode reconstruction plays a significant role in interpreting and analyzing the nonstationary signal constituted of various modes. However, it is difficult for most previous methods to handle signal modes with closely-spaced or spectrally-overlapped instantaneous frequencies (IFs) especially in adverse environments. To address this issue, we propose an enhanced TFR and mode decomposition (ETFR-MD) method, which is particularly adapted to represent and decompose multi-mode signals with close or crossing IFs under low signal-to-noise ratio (SNR) conditions. The emphasis of the proposed ETFR-MD is placed on accurate IF and instantaneous amplitude (IA) extraction of each signal mode based on short-time Fourier transform (STFT). First, we design an initial IF estimation method specifically for the cases involving crossing IFs. Further, a low-complexity mode enhancement scheme is proposed so that enhanced IFs better fit underlying IF laws. Finally, the IA extraction from signal's STFT coefficients combined with the enhanced IFs enables us to reconstruct each signal mode. In addition, we derive mathematical expressions that reveal optimal window lengths and separation interference of our method. The proposed ETFR-MD is compatible with previous related methods, thus can be regarded as a step toward a more general time-frequency representation and decomposition method. Experimental results confirm the superior performance of the ETFR-MD when compared to a state-of-the-art benchmark.
