摘要
为了解决现有参数估计方法中时频聚集性不强,低信噪比下估计精度不高问题,提出了一种基于二次迭代稀疏重构的跳频信号参数估计方法。根据跳频信号的时频稀疏性进行稀疏重构,获取信号的时频分布矩阵;通过分析时频分布矩阵的特点,对信号进行二次迭代稀疏重构,获取二次时频分布矩阵。为了提高在低信噪比下算法性能,采用二值形态学滤波对时频图进行处理,进而实现信号的良好参数估计。仿真结果表明:该算法能够有效地提高参数估计精度,在低信噪比下有良好的估计效果。
In order to solve the problem that the time-frequency aggregation is not strong in the existing parameter estimation method and the estimation accuracy is low under low SNR,a parameter estimation method of frequency hopping signal based on quadratic iterative sparse reconstruction(QISR) is proposed.Firstly,sparse reconstruction is performed according to the time-frequency sparseness of the frequency-hopping signal,and the time-frequency distribution matrix of the signal is obtained.Then,by analyzing the characteristics of the time-frequency distribution matrix,the signal is subjected to quadratic iterative sparse reconstruction to obtain the quadratic time-frequency distribution matrix.In addition,in order to improve the performance of the algorithm at low SNR,binary morphological filtering is used for processing of the time-frequency diagram.Then good parameter estimation of the signal is achieved.The simulation results show that the proposed algorithm can effectively improve the precision of parameter estimation and has a good estimation effect under low SNR.
作者
杨鑫
郭英
YANG Xin;GUO Ying(School of Information and Navigation,Air Force Engineering University,Xi'an 710077,China;Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory ,Shijiazhuang 050081 , China)
出处
《传感器与微系统》
CSCD
2019年第10期44-46,50,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61601500)
全军研究生资助项目(JY2018C169)
关键词
二次迭代稀疏重构
近似范数
二值形态学滤波
quadratic iterative sparse reconstruction
approximate l0 norm
binary morphological filtering
