Fast-slow Time Domain Joint Processing Suppressing Smeared Spectrum Jamming

Liang ZHANG; Guohong WANG; Xiangyu ZHANG; Siwen LI

doi:10.11999/JEIT190734

Volume 42 Issue 10

Oct. 2020

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(10): 2508-2515

HUANG Xiaoge, DENG Xuesong, CHEN Qianbin, ZHANG Jie. Asynchronous Federated Learning via Blockchain in Edge Computing Networks[J]. Journal of Electronics & Information Technology, 2024, 46(1): 195-203. doi: 10.11999/JEIT221517

Citation:

Liang ZHANG, Guohong WANG, Xiangyu ZHANG, Siwen LI. Fast-slow Time Domain Joint Processing Suppressing Smeared Spectrum Jamming[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2508-2515. doi: 10.11999/JEIT190734

Citation:

PDF( 2571 KB)

Fast-slow Time Domain Joint Processing Suppressing Smeared Spectrum Jamming

doi: 10.11999/JEIT190734

1.
Naval Aviation University, Yantai 264001, China
2.
Unit 94326 of the PLA, Jinan 250000, China

Funds: The National Natural Science Foundation of China (61731023, 61701519, 61671462), Taishan Scholar Climbing Plan

Received Date: 2019-09-24
Rev Recd Date: 2020-02-12

Available Online: 2020-03-03

Publish Date: 2020-10-13

Abstract

Abstract

The existing SMeared SPectrum (SMSP) jamming suppression algorithms take a jammed echo whose length equal to radar transmitting signal as the processing object and do not involve the whole echo within the coherent processing interval. For this problem, a jamming suppression algorithm based on fast and slow time domain joint processing is proposed under the background of Linear Frequency Modulation (LFM) coherent radar countering SMSP jamming. The time and frequency domain characteristics of SMSP are studied and the effect on coherent radar is analyzed on the condition of self screening jamming. On this basis, four processing steps are designed to suppress the SMSP jamming. Firstly, the jamming fast time location is estimated by calculating the differential entropy of slow time signal. Secondly, the real jamming parameter is found based on the maximum correlation coefficient criterion. Then the jamming signals are reconstructed using Biorthogonal Fourier Transform. Finally, the SMSP jamming is suppressed by cancellation. The simulation results show that the proposed algorithm model is highly consistent with the actual radar processing flow, and the efficiency is further verified through algorithms comparison.
- SMeared SPectrum (SMSP) jamming,
- Fast-slow time domain,
- Joint processing,
- Jamming cancellation

FullText(HTML)

References(17)

References

SPARROW M J and CIKALO J. ECM techniques to counter pulse compression radar[P]. United States Patent, 7081846, 2006.

赵杨, 尚朝轩, 韩壮志, 等. 分数阶傅里叶和压缩感知自适应抗频谱弥散干扰[J]. 电子与信息学报, 2019, 41(5): 1047–1054. doi: 10.11999/JEIT180569

ZHAO Yang, SHANG Chaoxuan, HAN Zhuangzhi, et al. Fractional Fourier transform and compressed sensing adaptive countering smeared spectrum jamming[J]. Journal of Electronics &Information Technology, 2019, 41(5): 1047–1054. doi: 10.11999/JEIT180569

卢云龙, 李明, 曹润清, 等. 联合时频分布和压缩感知对抗频谱弥散干扰[J]. 电子与信息学报, 2016, 38(12): 3275–3281. doi: 10.11999/JEIT160919

LU Yunlong, LI Ming, CAO Runqing, et al. Jointing time-frequency distribution and compressed sensing for countering smeared spectrum jamming[J]. Journal of Electronics &Information Technology, 2016, 38(12): 3275–3281. doi: 10.11999/JEIT160919

李欣, 王春阳, 原慧, 等. 基于干扰重构和峭度最大化的SMSP干扰抑制方法[J]. 北京航空航天大学学报, 2018, 44(6): 1176–1184. doi: 10.13700/j.bh.1001-5965.2017.0421

LI Xin, WANG Chunyang, YUAN Hui, et al. SMSP jamming suppression method based on jamming reconstruction and kurtosis maximum[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(6): 1176–1184. doi: 10.13700/j.bh.1001-5965.2017.0421

尹洪伟, 李国林, 路翠华. 一种基于复值盲分离的欺骗干扰抑制算法[J]. 上海交通大学学报, 2015, 49(10): 1564–1569. doi: 10.16183/j.cnki.jsjtu.2015.10.023

YIN Hongwei, LI Guolin, and LU Cuihua. An algorithm of deception jamming suppression based on complex-value Blind Source Separation[J]. Journal of Shanghai Jiaotong University, 2015, 49(10): 1564–1569. doi: 10.16183/j.cnki.jsjtu.2015.10.023

李飞, 李国林, 粘朋雷. 基于盲源分离的雷达信号欺骗干扰抑制[J]. 海军航空工程学院学报, 2015, 30(5): 424–428. doi: 10.7682/j.issn.1673-1522.2015.05.005

LI Fei, LI Guolin, and NIAN Penglei. Radar signal deception jamming suppressing based on Blind Source Separation[J]. Journal of Naval Aeronautical and Astronautical University, 2015, 30(5): 424–428. doi: 10.7682/j.issn.1673-1522.2015.05.005

ADAMY D L. EW 104: EW Against A New Generation of Threats[M]. Boston MA: Artech House, 2015: 60-64.

HUO Weibo, HUANG Yulin, PEI Jifang, et al. Ship detection from ocean SAR image based on local contrast variance weighted information entropy[J]. Sensors, 2018, 18(4): 1196. doi: 10.3390/s18041196

GÖKSU H. Ground moving target recognition using log energy entropy of wavelet packets[J]. Electronics Letters, 2018, 54(4): 233–235. doi: 10.1049/el.2017.4267

ZHANG Shuanghui, LIU Yongxiang, and LI Xiang. Autofocusing for sparse aperture ISAR imaging based on joint constraint of sparsity and minimum entropy[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(3): 998–1011. doi: 10.1109/JSTARS.2016.2598880

邹本振, 张萌, 王朝. 脉冲压缩雷达的信号包络及检测模型研究[J]. 中国电子科学研究院学报, 2019, 14(1): 55–60. doi: 10.3969/j.issn.1673-5692.2019.01.010

ZOU Benzhen, ZHANG Meng, and WANG Chao. Research on modeling of envelope detection of pulse-compression radar[J]. Journal of CAEIT, 2019, 14(1): 55–60. doi: 10.3969/j.issn.1673-5692.2019.01.010

王本庆, 李兴国. LFM信号调频斜率的双正交Fourier变换分析算法[J]. 电子与信息学报, 2009, 31(7): 1620–1623.

WANG Benqing and LI Xingguo. Analysis algorithm to frequency rate of LFM signal based on biorthogonal Fourier transform[J]. Journal of Electronics &Information Technology, 2009, 31(7): 1620–1623.

DE SENA A and ROCCHESSO D. A fast Mellin and scale transform[J]. EURASIP Journal on Advances in Signal Processing, 2007, 2007(1): 89170. doi: 10.1155/2007/89170

OZAKTAS H M, ARIKAN O, KUTAY M A, et al. Digital computation of the fractional Fourier transform[J]. IEEE Transactions on Signal Processing, 1996, 44(9): 2141–2150. doi: 10.1109/78.536672

HATEFFARD F, DOLATI P, HEIDARI A, et al. Assessing the performance of decision tree and neural network models in mapping soil properties[J]. Journal of Mountain Science, 2019, 16(8): 1833–1847. doi: 10.1007/s11629-019-5409-8

GARG H and RANI D. A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making[J]. Applied Intelligence, 2019, 49(2): 496–512. doi: 10.1007/s10489-018-1290-3

KRASICHKOV A S, GRIGORIEV E B, NIFONTOV E M, et al. Estimation of acceptable boundaries for the correlation coefficient in the ECG beat classification task[J]. Biomedical Engineering, 2018, 51(6): 389–393. doi: 10.1007/s10527-018-9756-5

Relative Articles

Supplements(0)

Cited By

Proportional views