改进对称共生矩阵的跳频信号盲提取方法

doi:10.3969/j.issn.1001-2400.2015.03.022

J4 ›› 2015, Vol. 42 ›› Issue (3): 129-134.doi: 10.3969/j.issn.1001-2400.2015.03.022

改进对称共生矩阵的跳频信号盲提取方法

古培;王斌

(解放军信息工程大学信息系统工程学院，河南郑州 450001)

收稿日期:2013-12-27 出版日期:2015-06-20 发布日期:2015-07-27
通讯作者: 古培
作者简介:古培(1988-)，男，解放军信息工程大学硕士研究生，E-mail：grainme@hotmail.com．
基金资助:
国家自然科学基金资助项目(61201381)

FH signal extraction using the improved symmetric co-occurrence matrix

GU Pei;WANG Bin

(Institute of Information Systems Engineering, PLA Information Engineering Univ., Zhengzhou 450001, China)

Received:2013-12-27 Online:2015-06-20 Published:2015-07-27
Contact: GU Pei

摘要/Abstract

摘要：

针对短波跳频信号盲检测中的去背景噪声和定频干扰问题，结合信号时频分析与共生矩阵阈值法，提出了一种基于噪声稀疏对称共生矩阵的跳频提取算法．首先对共生矩阵改进，定义了频率、时间方向上的噪声稀疏对称共生矩阵．然后根据频率稀疏共生矩阵估计背景噪声阈值，进而根据阈值从时间稀疏共生矩阵中提取跳频信号．仿真表明，该算法能实现低信噪比下跳频信号的盲提取，背景噪声阈值估计更为准确、稳定，跳频信号的提取效果好，且算法简单、运算量小，易于工程实现．

关键词: 跳频检测, 时频分析, 图像处理, 对称共生矩阵

Abstract:

Aiming at the problem of removing the background noise and fixed-frequency interference in the blind detection of FH signals from the HF channel, this paper proposes an FH signals extraction algorithm based on the noise-sparse symmetric co-occurrence matrix. Firstly, we define the noise-sparse symmetric co-occurrence matrix in the direction of frequency and time to improve the calculation of the co-occurrence matrix. Secondly, we estimate the noise threshold based on the frequency-sparse symmetric co-occurrence matrix and then extract the FH signals from the time-sparse co-occurrence matrix with the threshold. Simulation results show that the algorithm can realize the blind extraction of the FH signal on the condition of a low SNR. Estimation of the threshold in background noise is more accurate and stable. The performance of FH signal extraction is better and algorithm is simple with less computation and is easy to apply in engineering.

Key words: detection of FH signal, time-frequency analysis, image processing, symmetric co-occurrence matrix

古培;王斌. 改进对称共生矩阵的跳频信号盲提取方法[J]. J4, 2015, 42(3): 129-134.

GU Pei;WANG Bin. FH signal extraction using the improved symmetric co-occurrence matrix[J]. J4, 2015, 42(3): 129-134.

参考文献

［1］ Lehtomaki J J. Maximum Based Detection of Slow Frequency Hopping Signals［J］. IEEE Communications Letters , 2003, 7(5): 201-203.
［2］王国华, 俞能海. 基于快速折叠算法和时频分析的 LPI 跳频信号截获［J］. 电子与信息学报, 2007, 29(7): 1569-1572.
Wang Guohua, Yu Nenghai. FFA and TFA-based Interception of LPI Frequency Hopping Signal［J］. Journal of Electronics ＆ Information Technology, 2007, 29(7): 1569-1572.
［3］冯涛, 袁超伟. 基于时频脊线的跳频参数盲估计［J］. 电子学报, 2011, 39(12): 2921-2925.
Feng Tao, Yuan Chaowei. Blind Parameter Estimation of Frequency-hopping Signals Based on the Time-frequency Distribution Maxima［J］. Acta Electronica Sinica, 2011, 39(12): 2921-2925.
［4］曾祥峰, 杨育红, 崔鹏辉, 等. 组合谱图法在跳频信号时频分析中的应用［J］. 计算机工程与应用, 2013, 49(13): 89-92.
Zeng Xiangfeng, Yang Yuhong, Cui Penghui, et al. Applications of Combined Spectrogram to Frequency-hopping Signal Analysis［J］. Computer Engineering and Applications, 2013, 49(13): 89-92.
［5］李琰, 李天昀, 葛临东. 一种基于图像分割和模板匹配的短波跳频信号盲检测算法［J］. 信息工程大学学报, 2010, 11(6): 756-761.
Li Yan, Li Tanyun, Ge Lindong. Blind Detection of High Frequency FH Signals Based on Image Segmentation and Template Matching［J］. Journal of Information Engineering University, 2010, 11(6): 756-761.
［6］ Luo S, Luo L. Adaptive Detection of an Unknown FH Signal Based on Image Features［C］//5th International Conference on Wireless Communications, Networking and Mobile Computing. Piscataway: IEEE, 2009: 1-4.
［7］ Weszka J S, Rosenfeld A. Threshold Evaluation Techniques［J］. IEEE Transactions on Systems, Man and Cybernetics, 1978, 8(8): 622-629.
［8］任静, 范九伦. 对称共生矩阵阈值法的比较研究［J］. 西安邮电学院学报, 2011, 16(1): 1-5.
Ren Jing, Fan Jiulun. On the Symmetric Co-occurrence Matrix Thresholding Methods［J］. Journal of Xi'an Institute of Posts and Telecommunications, 2011, 16(1): 1-5.
［9］范九伦, 任静. 基于平方距离的对称共生矩阵阈值法［J］. 电子学报, 2011, 39(10): 2277-2281.
Fan Jiulun, Ren Jing. Symmetric Co-occurrence Matrix Thresholding Method Based on Square Distance［J］. Acta Electronica Sinica, 2011, 39(10): 2277-2281.
［10］张新明, 孙印杰, 郑延斌. 二维直方图准分的 Otsu 图像分割及其快速实现［J］. 电子学报, 2011, 39(8): 1778-1784.
Zhang Xinming, Sun Yinjie, Zheng Yanbin. Precise Two-Dimensional Otsu's Image Segmentation and Its Fast Recursive Realization［J］. Acta Electronica Sinica, 2011, 39(8): 1778-1784.

[1]	韩永赛,马时平,何林远,李承昊,朱明明,张飞. 改进YOLOv3的快速遥感机场区域目标检测[J]. 西安电子科技大学学报, 2021, 48(5): 156-166.
[2]	孙正阳,董玫,陈伯孝. 时频分析联合带通滤波抑制间歇采样转发干扰[J]. 西安电子科技大学学报, 2021, 48(2): 139-146.
[3]	李江,冯存前,王义哲,许旭光. 一种用于锥体目标微动分类的深度学习模型[J]. 西安电子科技大学学报, 2020, 47(3): 105-112.
[4]	成天佑,林艳萍,马晓军. 一种视触觉引导的超声探头自动定位方法[J]. 西安电子科技大学学报, 2020, 47(1): 80-87.
[5]	汪梓艺,苏育挺,刘艳艳,张为. 一种改进DeeplabV3网络的烟雾分割算法[J]. 西安电子科技大学学报, 2019, 46(6): 52-59.
[6]	朱苏雅,杜建超,李云松,汪小鹏. 采用U-Net卷积网络的桥梁裂缝检测方法[J]. 西安电子科技大学学报, 2019, 46(4): 35-42.
[7]	张锦涛,雷杰,吴凌云,黄碧莹,李云松. 一种改进的高光谱端元提取算法及其FPGA实现[J]. 西安电子科技大学学报, 2019, 46(4): 22-27.
[8]	赖睿,官俊涛,徐昆然,熊皑,杨银堂. 级联残差学习的红外图像非均匀性校正方法[J]. 西安电子科技大学学报, 2019, 46(1): 14-19.
[9]	郭松鸽;吕东辉;任艳丽. 一种改进的(k, n)标记视觉密码方案[J]. 西安电子科技大学学报, 2018, 45(5): 170-176+183.
[10]	黄勇;刘芳. 场景语义SAR图像桥梁检测算法[J]. 西安电子科技大学学报, 2018, 45(4): 40-44.
[11]	胡妍;王柯俨;许宁;李云松;张闪闪. 利用分割中值滤波和透射率补偿的图像去雾[J]. 西安电子科技大学学报, 2018, 45(4): 99-105.
[12]	王世平;毕笃彦;刘坤;何林远. 一种多映射卷积神经网络的超分辨率重建算法[J]. 西安电子科技大学学报, 2018, 45(4): 155-160.
[13]	冯玉颖;王俊平;马塾亮;李超;伍尧. COPLIP新模型及其图像增强新算法[J]. 西安电子科技大学学报, 2018, 45(1): 66-71.
[14]	倪洁;王俊平;杨国钰;伍尧;马塾亮. 一种柔性形态学参数对称对数图像处理新算法[J]. 西安电子科技大学学报, 2017, 44(5): 25-31+45.
[15]	孔繁锵;卞陈鼎;李云松;郭文骏. 非凸稀疏低秩约束的高光谱解混方法[J]. 西安电子科技大学学报, 2016, 43(6): 116-121.

改进对称共生矩阵的跳频信号盲提取方法

FH signal extraction using the improved symmetric co-occurrence matrix

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0