快慢時間域聯(lián)合處理抑制頻譜彌散干擾

張亮; 王國宏; 張翔宇; 李思文

doi:10.11999/JEIT190734

快慢時間域聯(lián)合處理抑制頻譜彌散干擾

doi: 10.11999/JEIT190734

張亮^{1, 2, ,},
王國宏¹,
張翔宇¹,
李思文¹

1.
海軍航空大學(xué) 煙臺 264001
2.
中國人民解放軍94326部隊濟南 250000

基金項目: 國家自然科學(xué)基金(61731023, 61701519, 61671462)，“泰山學(xué)者”攀登計劃專項經(jīng)費資助項目

詳細(xì)信息

作者簡介:
張亮：男，1987年生，博士生，研究方向為雷達(dá)抗干擾

王國宏：男，1963年生，教授，研究方向為多源信息融合、雷達(dá)組網(wǎng)、雷達(dá)抗干擾、微弱目標(biāo)跟蹤

張翔宇：男，1986年生，講師，研究方向為雷達(dá)抗干擾、雷達(dá)數(shù)據(jù)處理

李思文：男，1993年生，碩士生，研究方向為雷達(dá)抗干擾

通訊作者:
張亮　332401479@qq.com

中圖分類號: TN957.51
計量
- 文章訪問數(shù): 2697
- HTML全文瀏覽量: 1239
- PDF下載量: 109
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-09-24
- 修回日期: 2020-02-12
- 網(wǎng)絡(luò)出版日期: 2020-03-03
- 刊出日期: 2020-10-13

Fast-slow Time Domain Joint Processing Suppressing Smeared Spectrum Jamming

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

摘要

摘要: 現(xiàn)有頻譜彌散干擾(SMSP)抑制算法以一個長度為雷達(dá)發(fā)射信號的受干擾回波為處理對象，未涉及相參處理間隔內(nèi)整體回波。針對此問題，該文以自衛(wèi)式干擾條件下線性調(diào)頻(LFM)相參體制雷達(dá)抗SMSP干擾為背景，提出快慢時間域聯(lián)合處理抑制SMSP干擾算法。分析了SMSP干擾時頻特征和對相參雷達(dá)的干擾特性，在此基礎(chǔ)上，設(shè)計了慢時間微分熵估計干擾位置，相關(guān)系數(shù)最大準(zhǔn)則估計干擾參數(shù)，雙正交傅里葉變換快時間分段重構(gòu)干擾信號和干擾對消的抑制流程。仿真結(jié)果表明，所提算法模型與雷達(dá)處理流程切合度高，對比分析進(jìn)一步驗證算法效能。
- 頻譜彌散干擾 /
- 快慢時間域 /
- 聯(lián)合處理 /
- 干擾對消
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

HTML全文

圖 1 SMSP干擾時域波形

下載: 全尺寸圖片幻燈片

圖 2 雷達(dá)發(fā)射信號和SMSP干擾BFT結(jié)果

下載: 全尺寸圖片幻燈片

圖 3 SMSP干擾特性

下載: 全尺寸圖片幻燈片

圖 4 SMSP干擾快時間位置估計

下載: 全尺寸圖片幻燈片

圖 5 SMSP干擾參數(shù)估計

下載: 全尺寸圖片幻燈片

圖 6 SMSP干擾與重構(gòu)信號對比

下載: 全尺寸圖片幻燈片

圖 7 SMSP干擾抑制效果

下載: 全尺寸圖片幻燈片

圖 8 干擾前沿估計MAE隨JSR變化曲線

下載: 全尺寸圖片幻燈片

圖 9 干擾參數(shù)估計準(zhǔn)確率PEA隨JSR變化曲線

下載: 全尺寸圖片幻燈片

圖 10 真實SMSP與重構(gòu)信號PCC隨JSR變化曲線

下載: 全尺寸圖片幻燈片

圖 11 平均干擾抑制率MSR隨JSR變化曲線

下載: 全尺寸圖片幻燈片

參考文獻(xiàn)(17)

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

趙楊, 尚朝軒, 韓壯志, 等. 分?jǐn)?shù)階傅里葉和壓縮感知自適應(yīng)抗頻譜彌散干擾[J]. 電子與信息學(xué)報, 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

盧云龍, 李明, 曹潤清, 等. 聯(lián)合時頻分布和壓縮感知對抗頻譜彌散干擾[J]. 電子與信息學(xué)報, 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, 王春陽, 原慧, 等. 基于干擾重構(gòu)和峭度最大化的SMSP干擾抑制方法[J]. 北京航空航天大學(xué)學(xué)報, 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

尹洪偉, 李國林, 路翠華. 一種基于復(fù)值盲分離的欺騙干擾抑制算法[J]. 上海交通大學(xué)學(xué)報, 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

李飛, 李國林, 粘朋雷. 基于盲源分離的雷達(dá)信號欺騙干擾抑制[J]. 海軍航空工程學(xué)院學(xué)報, 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

G?KSU 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

鄒本振, 張萌, 王朝. 脈沖壓縮雷達(dá)的信號包絡(luò)及檢測模型研究[J]. 中國電子科學(xué)研究院學(xué)報, 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

王本慶, 李興國. LFM信號調(diào)頻斜率的雙正交Fourier變換分析算法[J]. 電子與信息學(xué)報, 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

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問統(tǒng)計