西安电子科技大学学报（自然科学版）2017，Vol.44Issue(3)：77-82,6.DOI:10.3969/j.issn.1001-2400.2017.03.014
一种深度学习的雷达辐射源识别算法
Radar emitter identification algorithm based on deep learning
摘要
Abstract
Aimed at the deficiency of traditional techniques of radar emitter feature extraction which rely heavily on artificial experience,a novel emitter identification algorithm based on joint deep time-frequency features is proposed.Time-domain signals are transformed into the 2-D time-frequency domain,and dimensionality reduction is implemented with random projection and principal component analysis with respect to sustaining subspace and energy.In the phase of pre-training,the deep model is layer-wise trained with unlabelled samples and network parameters are fine-tuned with label information.Finally the identification task is achieved with a logistic regression classifier.6 types of emitter signals are adopted in simulation experiments to validate the effectiveness of the proposed algorithm,the experimental results indicating that the joint deep features help to obtain higher identification accuracy and that the algorithm is more efficient.关键词
时频分布/降维/层叠自动编码器/深度学习/雷达辐射源识别Key words
time-frequency distribution/dimensionality reduction/stacked auto-encoder/deep learning/radar emitter identification分类
信息技术与安全科学引用本文复制引用
周志文,黄高明,高俊,满欣..一种深度学习的雷达辐射源识别算法[J].西安电子科技大学学报（自然科学版）,2017,44(3):77-82,6.基金项目
国家自然科学基金资助项目(61501484) （61501484）
国家“863”高技术研究发展计划资助项目(2014AA7014061) （2014AA7014061）
