| 引用本文: | 陈游,周一鹏,王星,等.采用区分性幅相联合字典学习的低截获概率信号分离方法.[J].国防科技大学学报,2019,41(3):18-24.[点击复制] |
| CHEN You,ZHOU Yipeng,WANG Xing,et al.Low probability of intercept signal separation method using discriminative amplitude-phase dictionary learning[J].Journal of National University of Defense Technology,2019,41(3):18-24[点击复制] |
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| 采用区分性幅相联合字典学习的低截获概率信号分离方法 |
| 陈游1, 周一鹏1, 王星1, 田元荣2, 周东青3 |
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(1. 空军工程大学 航空工程学院， 陕西 西安 710038;2. 国防科技大学 电子对抗学院， 安徽 合肥 230037;3. 北方电子设备研究所， 北京 100089)
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| 摘要: |
| 为解决采用字典学习的信号分离方法存在的相位信息缺失和子字典交叉表示问题，提出一种区分性幅相联合字典学习方法。该方法针对相位信息缺失问题，构建了幅相联合字典模型；针对混合信号在联合字典上投影时存在的交叉表示问题，基于区分性字典学习思想提出在字典学习过程目标函数中加入交叉表示抑制项。仿真结果表明：幅相联合字典能够充分表示典型低截获概率信号的幅相信息，交叉表示抑制项能有效抑制信号间的交叉表示，算法具有良好的分离性能。 |
| 关键词: 信号分离 字典学习 稀疏表示 低截获概率信号 |
| DOI：10.11887/j.cn.201903004 |
| 投稿日期：2018-01-08 |
| 基金项目:航空科学基金资助项目（20152096019） |
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| Low probability of intercept signal separation method using discriminative amplitude-phase dictionary learning |
| CHEN You1, ZHOU Yipeng1, WANG Xing1, TIAN Yuanrong2, ZHOU Dongqing3 |
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(1. Aeronautics Engineering College, Air Force Engineering University, Xi′an 710038, China;2. College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China;3. Northern Institute of Electronic Equipment of China, Beijing 100089, China)
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| Abstract: |
| In order to solve the shortcomings of signal separation methods based on the dictionary learning in phase information loss and cross representation, a signal separation algorithm based on the discriminative amplitude-phase dictionary learning was proposed. In discriminative amplitude-phase dictionary learning method, a model of amplitude-phase dictionary was proposed to solve the problem of phase information loss. Meanwhile, based on the idea of discriminative dictionary learning, a penalty term of cross representation was added into the object function of dictionary learning to solve the problem of cross representation, which happens to the mixed signal projected in joint dictionary. Experiment results show that the amplitude and phase information of low probability of intercept signals can be fully represented by amplitude-phase dictionaries. Meanwhile, the proposed penalty term within discriminative amplitude phase dictionary learning algorithm can profitably restrain the cross representation between signals and the proposed algorithm has a significant performance in signal separation. |
| Keywords: signal separation dictionary learning sparse representation low probability of intercept signal |
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