基于多径干扰认知的汽车毫米波雷达自适应波形优化方法

Adaptive millimeter-wave automotive radar waveform optimization based on learning of multi-path interference

随着毫米波雷达在汽车辅助驾驶系统及无人驾驶车辆中的广泛应用,在交通道路环境下工作的雷达越来越密集,其间产生的多径干扰会导致雷达出现虚假目标.为此,提出了一种基于多径干扰认知的雷达自适应抗干扰波形优化方法.首先,阐述了邻近的汽车雷达产生的多径干扰是一个动态随机过程,借助时变模型对雷达产生的多径干扰传输信道参数的动态演变过程进行了描述,利用卡尔曼滤波对邻近雷达产生的多径干扰模型参数进行在线认知学习;然后,建立主雷达的目标回波信号与干扰信号的模型,对多径干扰信号进行白化处理;最后,基于最大化信号与干扰比(Signal to interference ratio,SIR)准则,建立抗干扰雷达信号优化模型,进行雷达波形的自适应优化更新.仿真实验结果表明所提方法可有效地抑制雷达间的强干扰.

As millimeter wave radars（MMR）have been widely used in advanced driver assistance systems and autonomous driving vehicle to improve traffic safety,the radars may operate in a small road and in the same frequency band.Therefore,multi-path interference between different automotive radars arises,resulting in ghost targets.To deal with the emerging challenge of the multi-path interference,this paper presents an adaptive approach of MMR waveform optimization based on learning of multi-path interference.Firstly,the paper assumes that the multi-path interference follows a time-varying stochastic process;Thanks to a time-varying model,the coefficients of the multipath interference channels are dynamically modeled as a kind of finite impulse response;And then the parameters of the model of the neighboring multi-path interference are learnt by using a Kalman filter,based on sequential Bayesianinference.Secondly,the models of the desired radar signal and the multi-path interference are built.And the multipath interference signals are whitening filtered.Finally,based on knowledge of the neighboring multi-path interference and a criteria of maximum signal to interference ratio（SIR）,the anti-interference radar signal optimization model is generated,and therefore the MMR waveform is further optimized.The experimental results demonstrate that the proposed approach can significantly reduce neighboring multi-path interference,showing apromise for potential applications in automotive radar.