基于调频连续波雷达的多维信息特征融合人体姿势识别方法

Human Posture Recognition Based on Multi-dimensional Information Feature Fusion of Frequency Modulated Continuous Wave Radar

为实现在复杂多样的环境下人体姿势的识别,该文提出一种基于调频连续波(FMCW)雷达的多维信息特征融合的人体姿势识别方法。该方法通过对FMCW雷达原始信号进行3维快速傅里叶变换得到目标距离、速度和角度的多维信息,在采用具有噪声的基于密度的聚类算法(DBSCAN)和Hampel滤波算法解决运动范围内动态或静态目标的噪声干扰后使用卷积神经网络对多维信息进行特征提取,然后利用低秩多模态融合网络(LMF)充分融合多维信息的特征,并通过域鉴别器进一步获得与环境无关的特征,最终使用活动识别器获得姿势识别结果。为了实用性,在边缘计算平台上搭载预先设计的算法和训练好的网络模型进行实验验证。实验结果表明,在复杂的环境下该方法的识别精度可达到91.5%。

In order to realize the recognition of human posture in complex and diverse environments,a method based on Frequency Modulated Continuous Wave(FMCW)radar signal is proposed.This method obtains multi-dimensional information of distance,speed and angle by performing 3D fast Fourier transform on the original signal of FMCW radar.After using the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm and the Hampel filter algorithm to solve the noise interference of dynamic or static targets in the range of motion,the convolutional neural network is used to extract the features of multidimensional information.Then Low-rank Multimodal Fusion network(LMF)is used to fully fuse the features of multi-dimensional information.Finally,the domain discriminator further obtains domain-independent features,and the activity recognizer obtains the result of gesture recognition.The pre-designed algorithm and the trained network model are carried out on the edge computing platform for experimental verification.Experimental results show that the recognition accuracy of this method can reach 91.5%in complex environments.