[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 基于堆栈稀疏自编码器和微动特征的身份认证技术-Authentication Technology via Stack Sparse Autoencoder and Micro Motion Feature
文章摘要
袁延鑫,孙莉,张群.基于堆栈稀疏自编码器和微动特征的身份认证技术[J].空军工程大学学报:自然科学版,2018,19(4):48-53
基于堆栈稀疏自编码器和微动特征的身份认证技术
Authentication Technology via Stack Sparse Autoencoder and Micro Motion Feature
  
DOI:10.3969/j.issn.1009-3516.2018.04.009
中文关键词: 堆栈稀疏自编码器  特征提取  微动特征  身份认证
英文关键词: stack sparse autoencoder  feature extraction  micro motion feature  identity authentication
基金项目:国家自然科学基金(61701531);航空科学基金(20121996016);陕西省统筹创新工程特色产业创新链项目(2015KTTSGY0406)
作者单位
袁延鑫,孙莉,张群 空军工程大学信息与导航学院,西安,710077 
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中文摘要:
      从人体目标雷达回波数据中提取可分性较好的微动特征是实现目标分类的关键。针对传统谱图结构特征法对体型相似的人体目标精细识别,提出了基于堆栈稀疏自编码器的人体身份认证方法。首先构造堆栈稀疏自编码器网络,利用人体微动数据进行监督预训练,在不同层提取人体微动特征,然后将得到的特征输入softmax分类器进行有监督训练,用交叉验证调整网络参数,最后用训练好的网络进行人体目标分类。在不同人走路实测数据集上,3人平均识别率达到了83%,优于提取谱图结构特征分类的方法。
英文摘要:
      Extracting micro motion features from human radar echo data is a key to human target classification. Aimed at the problem that the traditional spectrum structure is hard to realize the fine recognition of similar body size, a method of human body identity authentication based on stack sparse autoencoder is proposed. First of all, this paper constructs a stack sparse self encoder network, performs unsupervised pre training by using human micro motion data, and extracts human micro motion features at different layers. Then the paper inputs the features into the softmax classifier for supervised training, and adjusts the network parameters by cross validation. Finally, the paper uses the trained network for human target classification. The average recognition rate of 3 people on the measured data set of different people reaches 83%, and is better than that by the method of extracting spectral structure feature classification.
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