论文标题
VAFER：基于信号分解的FMCW雷达中的相互干扰抑制
VAFER: Signal Decomposition based Mutual Interference Suppression in FMCW Radars
论文作者
论文摘要
随着频率调节连续波（FMCW）雷达在自动驾驶汽车中的应用增加，FMCW雷达之间的相互干扰构成了严重的威胁。通过本文，我们提出了一种新颖的方法，可有效且优雅地抑制FMCW雷达中的相互干扰。我们首先使用变分模式分解（VMD）将接收的信号分解为模式，并使用傅立叶同步转换（FSST）执行时频分析。然后，通过在VMD模式的时频光谱上应用了提出的基于能量透射的阈值操作来重建干扰抑制的信号。在存在FMCW干扰的情况下，根据信噪比加噪声比（SINR）（SINR）（SINR）（SINR）（SINR）（SINR）（SINR）（SINR）（SINR）（SINR）和相关系数测量的有效性。与其他现有文献相比，我们提出的方法表明，对于模拟数据，至少14.07 dB的输出SINR和实验数据的9.87 dB的显着改善。
With increasing application of frequency-modulated continuous wave (FMCW) radars in autonomous vehicles, mutual interference among FMCW radars poses a serious threat. Through this paper, we present a novel approach to effectively and elegantly suppress mutual interference in FMCW radars. We first decompose the received signal into modes using variational mode decomposition (VMD) and perform time-frequency analysis using Fourier synchrosqueezed transform (FSST). The interference-suppressed signal is then reconstructed by applying a proposed energy-entropy-based thresholding operation on the time-frequency spectra of VMD modes. The effectiveness of proposed method is measured in terms of signal-to-interference plus noise ratio (SINR) and correlation coefficient for both simulated and experimental automotive radar data in the presence of FMCW interference. Compared to other existing literature, our proposed method demonstrates significant improvement in the output SINR by at least 14.07 dB for simulated data and 9.87 dB for experimental data.
