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Iterative modified threshold method based on EMD for interference suppression in FMCW radars

机译:基于EMD在FMCW雷达中干扰抑制的迭代修改阈值方法

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摘要

The mutual interference between frequency-modulated continuous-wave (FMCW) automotive radars increases the noise floor, even submerges the information of targets, and creates ghost targets. To reduce the impact of the mutual interference between automotive radars and improve the detection performance, this study proposes an iterative modified threshold method based on empirical mode decomposition (IMT-EMD) for interference suppression in FMCW automotive radars. The IMT-EMD uses the consecutive mean square error algorithm to determine the interference-dominated components after decomposing, which are adjusted according to the modified threshold. The proposed method can mitigate the interference effectively while keeping the signals continuous and reducing the loss of useful signals as much as possible. To further improve the detection performance, the authors used the proposed algorithm iteratively. The improvement of the signal-to-noise ratio of targets will converge to an optimal value when the number of iterations reaches a certain value after using the IMT-EMD. The results of simulation and field experiments showed that the proposed algorithm has a satisfactory effect on interference suppression and improves detection performance.
机译:频率调制的连续波(FMCW)汽车雷达之间的相互干扰增加了噪声底板,甚至潜水到目标的信息,并创造了幽灵目标。为了减少汽车雷达之间的相互干扰的影响,提高检测性能,本研究提出了一种基于经验模式分解(IMT-EMD)的迭代修正阈值方法,用于FMCW汽车雷达中的干扰抑制。 IMT-EMD使用连续均方误差算法来确定分解后的干扰主导的组件,根据修改的阈值调整。所提出的方法可以有效地减轻干扰,同时保持信号连续并尽可能地降低有用信号的损耗。为了进一步提高检测性能,作者迭代地使用所提出的算法。当使用IMT-EMD后迭代的数量达到一定值时,目标的信噪比的提高将收敛到最佳值。模拟和现场实验结果表明,该算法对干扰抑制效果令人满意,提高了检测性能。

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