基于EMD的车载雷达抗干扰研究

Research on Interference Mitigation of Automotive Radar Based on EMD

随着高级驾驶辅助系统的发展,毫米波雷达被广泛应用于汽车上,但随着道路上雷达的增多,雷达抗干扰也成为一个研究热点。线性调频连续波雷达之间的相互干扰可以分为交叉干扰和平行干扰,交叉干扰会使得本地噪声增加,降低目标的信噪比进而影响目标检测,平行干扰会产生虚假目标,但是出现的概率比较小。为了解决交叉干扰带来的影响,提出了经验模态分解和自回归模型相结合的干扰抑制方法。首先将含有干扰的回波信号进行经验模态分解,通过本征模态函数的自相关函数能量找出有用信号主导的模态和干扰信号主导的模态。其次对干扰信号主导的模态采用阈值法进行干扰点检测,并采用自回归模型的方法对干扰点的值进行恢复。最后将所有的模态加起来得到干扰抑制后的信号。实验结果显示,该方法能有效地降低频域噪声基底提高目标的信噪比。

With the development of advanced driver assistance systems,millimeter wave radar is widely used in automobiles.But as more and more radars are on the road,radar interference has also become a research hotspot.The mutual interference between LFMCW radar can be divided into cross interference and parallel interference.Cross interference will increase the noise floor,reduce the signal-to-noise ratio of the target and affect the target detection.Parallel interference will generate a ghost target in rare situations.In order to solve the influence of cross interference,an interference suppression method combining empirical mode decomposition and autoregressive model is proposed.Firstly,the echo signal containing interference is decomposed by empirical mode,and the mode dominated by the useful signal and the mode dominated by the interference signal is found through the autocorrelation function energy of the intrinsic mode function.Secondly,the threshold method is used to detect the interference point for the mode dominated by the interference signal,and the autoregressive model method is used to restore the value of the interference point.Finally,add up all the modes to get the signal after interference suppression.The experimental results show that this method can effectively reduce the frequency-domain noise floor and improve the signal-to-noise ratio of the target.