随着自动驾驶技术的发展,毫米波雷达成为了自动驾驶的一个关键传感器,由于车载雷达数量的增加,雷达间相互干扰是不可避免的,干扰可能会导致雷达检测性能降低,产生漏检和虚警的情况。为了解决干扰带来的问题,文中采用了一种在时-频域中使用经验模态分解和卡尔曼滤波相结合的干扰抑制方法。首先将时域干扰信号通过短时傅里叶变换转换到时-频域;然后将时-频域信号进行经验模态分解,将干扰主导的本征模态函数中的干扰部分置零,并将干扰置零后的本征模态和目标信号主导的本征模态进行相加;最后通过卡尔曼滤波对信号进行重构。实验结果显示,该方法不仅能够降低频域中噪声,而且能够提高目标的信噪比,增加了目标被检测概率。

With the development of automatic driving technology, millimeter wave radar has become a key sensor of automatic driving. Due to the increase of the number of vehicular radars, mutual interference between radars is inevitable. Interference may lead to a decrease in radar detection performance, resulting in missed detections and false alarms. In order to solve the problems caused by interference, an interference suppression method combining empirical mode decomposition and Kalman filter in time-frequency domain is adopted in this paper. Firstly, the time domain interference signal is converted to the time-frequency domain through short-time Fourier transform, and then empirical mode decomposition is conducted on the time-frequency domain signal. The interference part in the interference dominated intrinsic mode function is set to zero, and the interference zeroed intrinsic mode is added to the target signal dominated intrinsic mode. Finally, the signal is reconstructed using Kalman filtering. The experimental results show that the proposed method can not only reduce the noise in the frequency domain, but also improve the signal-to-noise ratio of the target and increase the probability of target detection.

TN958.94

国家自然科学基金资助项目(61561010);广西创新驱动发展专项资助项目(桂科AA21077008);广西重点研发计划资助项目( 桂科AB18126003,AB18221016)