基于多天线FMCW雷达的人体行为识别方法

提出一种基于多天线调频连续波(frequency modulated continuous wave， FMCW)雷达的多参数融合神经网络(fusion neural network， FNN)人体行为识别方法。针对FMCW雷达参数估计算法角度分辨率不足以及在估计目标个数错误的情况下会降低精度的问题，提出一种结合最小功率无失真响应(minimum power distortionless response， MPDR)波束形成与快速傅里叶变换(fast Fourier transform， FFT)的距离-方位角参数联合估计算法。利用2个相互垂直的线阵雷达捕捉人体行为，使用参数联合估计算法估计人体目标各回波点在水平与垂直方向的距离、角度参数。构建FNN，从参数估计结果中提取并融合人体行为在水平与垂直方向的空间与时间特征，根据融合特征实现人体行为识别与分类。实验结果表明，FNN方法对人体行为识别的准确率相比传统三维卷积神经网络(3D convolutional neural networks， 3D-CNN)提升了4.37%。

Human action recognition based on multi-antenna FMCW radar

In this paper, a multi-parameter fusion neural network (FNN) for human actions recognition based on multi-antenna frequency modulated continuous wave (FMCW) radar is proposed. First, in view of the shortcomings existing in FMCW radar parameter estimation algorithms, we develop a joint range-azimuth parameter estimation method combining minimum power distortionless response (MPDR) beamforming with fast Fourier transform (FFT), which provides higher angular resolution and overcomes the problem of the performance degradation that most of the super-resolution algorithms tend to have under unknown target number. Second, this method uses two mutually perpendicular linear array radars to capture human actions, and then the distance as well as angle parameters of the signal reflection position of human target in the horizontal and vertical directions are estimated. After that, FNN is constructed to extract and fuse the spatial and temporal features of human action in the horizontal and vertical directions from the parameter estimation results, based on which this method realizes human actions recognition and classification. The extensive experimental results show that FNN proposed improves the recognition accuracy by 4.37% compared with the traditional 3D convolutional neural networks (3D-CNN).