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非合作跳频信号参数的盲压缩感知估计
引用本文:陈莹,钟菲,郭树旭. 非合作跳频信号参数的盲压缩感知估计[J]. 雷达学报, 2016, 5(5): 531-537. DOI: 10.12000/JR15106
作者姓名:陈莹  钟菲  郭树旭
作者单位:1.(吉林大学电子科学与工程学院 长春 130012)2.(长春工程学院 长春 130012)
基金项目:吉林省教育厅十二五科学研究项目(120150047)
摘    要:针对非合作跳频通信系统采样速率高,先验信息少等问题,论文提出基于盲压缩感知重构理论参数估计算法。利用稀疏编码与正交基变换交替迭代的思想实现信号精确重构,并根据重构结果直接对跳频信号进行参数估计。与传统的压缩感知理论相比,盲压缩感知理论避免了对信号先验信息的需求,有效解决了非合作通信系统中先验信息少的问题。首先,建立信号模型,然后利用正交块对角盲压缩感知算法(Orthonormal Block Diagonal Blind Compressed Sensing,OBD-BCS)实现信号的重构,并估算出跳变频率及跳变周期。通过实验分析,该方法可以在低信噪比环境下恢复信号原始结构及信息,完成参数估计。 

关 键 词:盲压缩感知   跳频信号   非合作   参数估计
收稿时间:2015-09-21

Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal
Chen Ying,Zhong Fei,Guo Shuxu. Blind Compressed Sensing Parameter Estimation of Non-cooperative Frequency Hopping Signal[J]. Journal of Radars, 2016, 5(5): 531-537. DOI: 10.12000/JR15106
Authors:Chen Ying  Zhong Fei  Guo Shuxu
Affiliation:1.(College of Electronic Science & Engineering, Jilin University, Changchun 130012, China)2.(Changchun Institute of Technology, Changchun 130012, China)
Abstract:To overcome the disadvantages of a non-cooperative frequency hopping communication system, such as a high sampling rate and inadequate prior information, parameter estimation based on Blind Compressed Sensing (BCS) is proposed. The signal is precisely reconstructed by the alternating iteration of sparse coding and basis updating, and the hopping frequencies are directly estimated based on the results. Compared with conventional compressive sensing, blind compressed sensing does not require prior information of the frequency hopping signals; hence, it offers an effective solution to the inadequate prior information problem. In the proposed method, the signal is first modeled and then reconstructed by Orthonormal Block Diagonal Blind Compressed Sensing (OBD-BCS), and the hopping frequencies and hop period are finally estimated. The simulation results suggest that the proposed method can reconstruct and estimate the parameters of non-cooperative frequency hopping signals with a low signal-to-noise ratio. 
Keywords:
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