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Robust time-varying filtering and separation of some nonstationary signals in low SNR environments

机译：在低SNR环境中对某些非平稳信号进行鲁棒的时变滤波和分离

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摘要

Conventional time-varying filtering is inefficient for severely spoiled signals because it requires discernible spectral content of the signal in time-frequency domain. Motivated by the time-frequency peak filtering (TFPF) algorithm, a robust time-varying filtering (RTVF) algorithm is proposed in this paper for the objectives of filtering and separating some nonstationary signals that contain strong noise. The performance of the TFPF based on windowed Wigner-Ville distribution is intrinsically limited by the linear constraint on the waveform of the received signal. The proposed RTVF significantly improves the filtering performance with low complexity by applying a sinusoidal time-frequency distribution, which allows a sinusoidal constraint on the signal's waveform. The bias analysis of the RTVF is presented for some nonstationary signals with time-varying amplitude. Based on derived bias expressions, a criterion of optimal window size selection for implementation purpose is obtained. The RTVF can successfully decompose some multi-component signal into individual components based on an initial instantaneous frequency (IF) estimate of each component. Unlike existing time-varying filters, the RTVF is much less sensitive to the accuracy of the initial IF estimate. Computer simulations verify the theoretical analysis and demonstrate that the RTVF algorithm can achieve desirable performance of filtering and separation in low SNR environments.
机译：常规的时变滤波对于严重损坏的信号效率不高，因为它需要在时频域中辨别信号的频谱内容。时频峰值滤波（TFPF）算法的启发，提出了一种鲁棒的时变滤波（RTVF）算法，其目的是对一些包含强噪声的非平稳信号进行滤波和分离。基于加窗Wigner-Ville分布的TFPF的性能本质上受到接收信号波形线性限制的限制。所提出的RTVF通过应用正弦时频分布，可以以较低的复杂度显着提高滤波性能，从而可以对信号波形进行正弦约束。针对某些具有随时间变化幅度的非平稳信号，对RTVF进行了偏差分析。基于导出的偏差表达式，获得用于实现目的的最佳窗口尺寸选择准则。 RTVF可以基于每个分量的初始瞬时频率（IF）估计，将某些多分量信号成功分解为单个分量。与现有的时变滤波器不同，RTVF对初始IF估计的准确性不那么敏感。计算机仿真验证了理论分析，并证明了RTVF算法可以在低SNR环境中实现理想的滤波和分离性能。

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