基于随机森林的脉冲超宽带雷达微动手势识别

Hand Gesture Recognition Using Ultra-Wideband Radar with Random Forest

针对雷达微动手势识别中的回波信噪比低、数据量大、特征可解释性差的问题,提出了一种基于随机森林的超宽带雷达微动手势识别系统。微动手势雷达截面积小,进而导致信噪比低、正向特征模糊等问题。针对这些问题,采用聚类算法提取回波主向量并构建多项式特征,以减少冗余数据,提高手势回波信噪比。对于训练过程中特征图谱可解释性破坏的问题,采用随机森林可视化特征贡献率并以此选择特征应用于模型。实验结果表明,在不同底噪的回波信号下,该算法相比于其他算法具有更好的识别性能,这验证了算法的有效性。

Aiming at the problems of low echo signal-to-noise ratio,large amount of data,and weak interpretability of features in radar micro-motion gesture recognition,a micro-motion gesture recognition system using an ultra-wideband radar based on random forest is proposed.The small radar cross section of the micro-motion gesture causes problems such as low signal-to-noise ratio and blurred positive features.As for these problems,the clustering algorithm is used to extract the main vector of echo and construct polynomial features to reduce redundant data and improve the signal-to-noise ratio of gesture echo signals.For the destruction of interpretability during training process of feature maps,random forest is used to visualize the feature contribution rate and select features for applying to the model.Experimental results show that the algorithm has better recognition performance than other algorithms under echo signals with different noise floors,which verifies the effectiveness of the algorithm.