郭英1,
张东伟2,
杨银松3,
齐子森1,
眭萍1
1.空军工程大学信息与导航学院 西安 710077
2.空军工程大学空管领航学院 西安 710051
3.空军通信士官学校综合训练系 大连 116100
详细信息
作者简介:李红光：男，1986年生，工程师，博士，研究方向为信息对抗理论、通信信号处理
郭英：女，1961年生，教授，博士，研究方向为信息对抗理论、通信信号处理、自适应信号处理
张东伟：男，1987年生，讲师，博士，研究方向为通信信号处理
杨银松：男，1994年生，助教，硕士，研究方向为通信信号处理、电子对抗装备维修
齐子森：男，1982年生，副教授，博士，研究方向为通信信号侦察处理、阵列信号处理
眭萍：女，1991年生，工程师，博士，研究方向为通信信号侦察处理
通讯作者:李红光 toumingwings@163.com
中图分类号:TP391计量
文章访问数:352
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被引次数:0
出版历程
收稿日期:2019-11-15
修回日期:2020-12-29
网络出版日期:2021-01-08
刊出日期:2021-02-23
Synchronous Frequency Hopping Signal Network Station Sorting Based on Underdetermined Blind Source Separation
Hongguang LI1,,,Ying GUO1,
Dongwei ZHANG2,
Yinsong YANG3,
Zisen QI1,
Ping SUI1
1. Information and Navigation College, Air Force Engineering University, Xi’an 710077, China
2. Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China
3. Department of Comprehensive Training, Air Force Sergeant School of Communication, Dalian 116100, China
摘要
摘要:针对同步跳频(FH)网台分选问题，该文提出一种基于时频域单源点检测的欠定盲源分离(UBSS)分选算法。该算法首先对观测信号时频变换，利用自适应阈值去噪算法消除时频矩阵背景噪声，增加算法抗噪性能，然后根据信号绝对方位差算法进行单源点检测，有效保证单源点的充分稀疏性，并通过改进的模糊值聚类算法完成混合矩阵和2维波达方向估计，降低噪声和样本集分布差异对聚类结果的影响，提高估计精度。最后采用变步长的稀疏自适应子空间追踪(SASP)算法对源信号进行重构恢复。仿真实验表明，该算法在低信噪比(SNR)条件下，跳频信号波达方向估计和恢复精度较高，能够有效完成同步跳频信号的盲分离。
关键词:网台分选/
时频变换/
单源点检测/
混合矩阵
Abstract:Considering the problem of synchronous Frequency Hopping(FH) network station sorting, an Underdetermined Blind Source Separation(UBSS) algorithm based on time-frequency domain single source point detection is proposed. Firstly, the algorithm performs time-frequency transform on the observed signal, and uses adaptive threshold denoising algorithm to eliminate the background noise of the time-frequency matrix. It can increase the algorithm anti-noise performance. Then, single source point detection is performed according to the absolute azimuth difference of the signal. It can effectively ensure the sufficient sparsity of a single source point. The hybrid matrix estimation is completed by the improved fuzzy C value clustering algorithm. It can reduce the influence of noise and sample set distribution differences and improve the estimation accuracy. Finally, the source signal is reconstructed and restored by a variable step size Sparsity Adaptive Subspace Pursuit(SASP) algorithm. The simulation experiments show that the proposed algorithm has higher recovery accuracy of the frequency hopping signal under the condition of low Signal to Noise Ratio (SNR), and can effectively complete the blind separation of the synchronous frequency hopping signal.
Key words:Network sorting/
Time-frequency transform/
Single source point detection/
Mixed matrix
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