基于深度神经网络的微弱生命信号识别  

Weak Life Signal Recognition Based on Deep Neural Network

作　　者：李燕 李亮 赵晨宇 张玉禄 贺云 梁培 Li Yan;Li Liang;Zhao Chenyu;Zhang Yulu;He Yun;Liang Pei(College of Optical and Electronic Technology,China Jiliang University,Hangzhou 310000,Zhejiang,China;School of Electronics&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;School of Information Engineering,Wuhan University of Technology,Wuhan 430070,Hebei,China)

机构地区：[1]中国计量大学光学与电子科技学院,浙江杭州310000 [2]南京信息工程大学电子与信息工程学院,江苏南京210044 [3]武汉理工大学信息工程学院,湖北武汉430070

出　　处：《光学学报》2024年第21期216-225,共10页Acta Optica Sinica

基　　金：国家重点研发计划(2022YFF0606702);国家自然科学基金(22174133,1210042018);湖北省自然科学基金(2022CFB963)。

摘　　要：提出一种基于Trans-shrink-Net阈值收缩神经网络结合单元平均的恒虚警率算法进行前置处理的毫米波雷达微弱生命信号识别技术。该方法采用恒虚警检测算法提取雷达谱的生命信号特征,建立了数据量为80 Gbit的标准数据集,训练数据超100000条,同时结合Trans-shrink-Net神经网络学习微弱生命信号的全局分布特征,通过阈值残差块引入位置敏感性,经多层感知机输出生命信号预测结果。实测结果表明,所述方法可以有效提取微弱生命信号的特征并准确识别生命活体,在提升鲁棒性的前提下,对于微弱生命活体识别准确率可达96.17%。Objective In recent years,the escalation of globalization has heightened the threat of invasive species to the economies and ecosystems of various countries,emphasizing the importance of live detection in security.Frequency-modulated continuous wave(FMCW)radar technology has gradually matured as a non-contact method for life signal detection,offering solutions in fields such as biology and security.However,there remains a significant research gap in extracting and recognizing life signal characteristics from cold-blooded organisms like insects.Current physical feature extraction algorithms struggle with issues such as insufficient information and low accuracy when dealing with weak life signals,limiting the application of millimeter-wave radar technology.Therefore,we propose a method using the Transformer neural network architecture with embedded threshold shrinkage residual blocks,called the Trans-shrink-Net neural network,for life signal feature extraction and recognition.This approach aims to enhance the accuracy and generalization capability of millimeter-wave radar in detecting weak life signals.Additionally,we introduce the cell averaging-constant false alarm rate(CA-CFAR)algorithm as a preprocessing step to create high-quality standardized datasets,mitigating issues such as dirty data that could affect network performance.The significance of this method is twofold.First,by leveraging deep neural network technology,we can better explore and utilize the latent information in millimeter-wave radar data,improving the efficiency of life signal extraction and recognition.Second,this method addresses the current research gap in life signal detection,providing new avenues for development in related fields.Most importantly,applying this method will elevate the application level of millimeter-wave radar technology in detecting weak life signals,offering reliable technical support for ecological monitoring,disaster relief,and other areas.Overall,our research aims to propose a new method for life signal detection,addr

关 键 词：生物技术 毫米波雷达 信号特征提取 Transformer神经网络 活体信号识别 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]