基于FMCW雷达微多普勒室内人体感知方法

Indoor presence perception based on FMCW radar micro-doppler

针对室内场景下FMCW雷达在动态感知方面,存在识别静止人体常有丢失目标或与环境中微动物体互相干扰的问题,提出一种基于FMCW雷达微多普勒的人体存在感知的识别方法。首先基于FMCW雷达的采样数据立方体构建RDM(Range Doppler Map),通过投影累积的方式计算得出目标所在范围对应的雷达精确距离门子集;然后,基于距离门子集由短时傅里叶变换得到高精度微多普勒时频特征图,从中提取幅值特征,速度中心特征,速度带宽特征等共10个具备实际意义的特征矢量;最后,基于实测数据以支持向量机对不同特征矢量组合进行最优化筛选。实验结果表明,存在一种由其中5个特征矢量组成的最佳组合,使得对室内静止人体、环境扰动、走动人体的整体识别率最高,达到了97.98%,不仅提高了分类模型性能还增强了其可解释性。

A solution is proposed for human presence recognition in indoor scenarios using FMCW radar.There are often issues of target loss or interference with small moving objects in the environment when it comes to recognizing stationary individuals.Firstly,a Range Doppler Map(RDM)is constructed using the sampled data from FMCW radar,and the precise distance subset corresponding to the target's location range is calculated through cumulative projection.Then,a high-precision micro-Doppler time-frequency feature map is obtained based on the distance subset using Short-Time Fourier Transform(STFT),from which 10 meaningful feature vectors are extracted.Finally,experimental data combined with Support Vector Machines(SVM)is used to screen different combinations of feature vectors.The experimental results show that there exists an optimal combination of 5 feature vectors,achieving a recognition rate of 97.98%for overall recognition of stationary individuals,environmental disturbances,and walking individuals in indoor environments.This method not only improves the performance of the classification model but also enhances its interpretability.