车载分布式MIMO雷达原理与实测数据分析

Principle and Analysis of Measured Data on Vehicular Distributed MIMO Radar

文中分析了分布式构型多输入多输出(MIMO)雷达及其处理框架,并针对车载应用场景,结合超分辨测角算法给出了一套基于分布式MIMO雷达的成像、测速、跟踪处理流程。在此处理流程的基础上,文中创新性地提出了基于高斯混合概率假设密度(GM-PHD)跟踪算法的距离-角度-速度关联方案,并对GM-PHD算法进行了改进,增加了标签配对功能,同时实现了多维信息的快速关联和目标的航迹跟踪。最后,利用实测数据验证了相关算法的有效性和实用性,与单一雷达成像及跟踪结果对比并进行了详细分析,验证了分布式MIMO雷达具有更优的目标检测性能及目标跟踪效果。

In this paper the configuration and processing framework of distributed multiple input multiple output(MIMO)radar is analyzed,and a set of imaging,velocity measurement and tracking processing flow based on distributed MIMO radar is given for vehicle application scenarios,combined with super-resolution angle measurement algorithm.On the basis of this processing flow,in this paper a range-angle-velocity association scheme based on the Gaussian mixture probability hypothesis density(GM-PHD)tracking algorithm is innovatively proposed,and the GM-PHD algorithm is improved by adding the tag matching function.This method can simultaneously realize the fast association of multi-dimensional information and target track tracking.Finally,the effectiveness and practicability of the related algorithms are verified by using the measured data.Compared with the single radar imaging and tracking results,the distributed configuration has better target detection and target tracking effects.