基于分數(shù)階傅里葉變換的窄帶雷達飛機目標回波特征提取方法
doi: 10.11999/JEIT161035
基金項目:
國家自然科學基金(61271024, 61322103)，高等學校博士學科點專項科研基金博士生導師類基金(20130203110013)，陜西省自然科學基礎研究計劃(2015JZ016)
Feature Extraction Method of Narrow-band Radar Airplane Signatures Based on Fractional Fourier Transform
Funds:
The National Natural Science Foundation of China (61271024, 61322103), The Foundation for Doctoral Supervisor of China (20130203110013), The Natural Science Foundation of Shaanxi Province (2015JZ016)
-
摘要: 該文研究了常規(guī)窄帶雷達體制下，利用分數(shù)階傅里葉變換擴展特征域，從而解決直升機、螺旋槳飛機和噴氣式飛機3類飛機目標回波分類中的特征提取問題。在現(xiàn)代戰(zhàn)場中，直升機、螺旋槳飛機和噴氣式飛機具有不同的機動性能，并各自承擔著重要的任務。因此，實現(xiàn)這3類飛機的分類具有重大的意義。該文針對3類飛機目標分類的傳統(tǒng)特征數(shù)目少，包含信息量有限，導致分類性能不夠好的問題，基于現(xiàn)有的特征提取方法引入分數(shù)階傅里葉變換(Fractional Fourier Transform, FrFT)，在經(jīng)過FrFT后的分數(shù)域提取3類飛機目標回波的分數(shù)階特征，彌補傳統(tǒng)特征的不足。并利用線性相關(guān)向量機(Relevance Vector Machine, RVM)的特征選擇功能對提取的分數(shù)階特征進行特征選擇并分類?；诜抡婧蛯崪y數(shù)據(jù)的實驗結(jié)果證明該文提出的分數(shù)階特征的分類性能較傳統(tǒng)時域、多普勒域特征有較大提升。
-
關(guān)鍵詞:
- 窄帶雷達 /
- 分數(shù)階傅里葉變換 /
- 特征提取 /
- 特征選擇 /
- 目標分類 /
- 噴氣引擎調(diào)制
Abstract: This paper studies on the feature extraction methods for the classification of helicopter, propeller-driven aircraft, and turbojet using a conventional narrow-band radar system. In the modern battlefield, the helicopter, propeller aircraft and jet aircraft with different motor performances each bear an important task. But the classification performance of the traditional features for the three types of aircraft target classification is not good enough, so the Fractional Fourier Transform (FrFT) is introduced. Based on the existing feature extraction method, the fractional order features of three kinds of aircraft targets are extracted from the fractional domain after FrFT to extend feature domain. Then, the effective features are selected from all extracted features and the classification of the three categories via linear Relevance Vector Machine (RVM) is realized. The experiments demonstrate that the proposed fractional features can improve the classification performance in comparison with some existing features from the time-domain and Doppler-frequency domain. -
CHEN V C. Radar signatures of rotor blades[J]. SPIE, 2001, 4391(1): 63-70. doi: 10.1117/12.421231. YONG Y W, HOON P J, WOO B J, et al. Automatic feature extraction from jet engine modulation signals based on an image processing method[J]. IET Radar, Sonar Navigation, 2015, 9(7): 783-789. doi: 10.1049/iet-rsn.2014. 0281. 杜蘭, 李林森, 李瑋璐, 等. 基于時域回波相關(guān)性特征的飛機目標分類方[J]. 雷達學報, 2015, 4(6): 621-629. doi: 10.12000 /JR15117. DU Lan, LI Linsen, LI Weilu, et al. Aircraft target classification based on correlation features from time domain echoes[J]. Journal of Radars, 2015, 4(6): 621-629. doi: 10. 12000/JR15117. 陳娟. 基于多特征融合的雷達目標識別[D]. [碩士論文], 西安電子科技大學, 2010. CHEN Juan. Radar target recognition based on multi- features fusion[D]. [Master dissertation], Xidian University, 2010. 陶然, 齊林, 王越. 分數(shù)階Fourier變換的原理與應用[M]. 北京: 清華大學出版社, 2004: 3-4, 31-45. TAO Ran, QI Lin, and WANG Yue. Principle and Application of Fractional Fourier Transform[M]. Beijing: Tsinghua University Press, 2004: 3-4, 31-45. OZAKTAS H M and BARSHAN B. Convolution, filtering, and multiplexing in fractional Fourier domains and their relation to LFM and wavelet transforms[J]. Journal of the Optical Society of America A-Optics Image Science and Vision, 1993, 11(2): 547-559. doi: 10.1364/JOSAA.11. 000547. PENG Hsiaowei, CHANG Hsuanting, and LIN Chingchou. 2-D linear frequency modulation signal separation using fractional Fourier transform[C]. International Symposium on Computer Consumer and Control, Xian, 2016: 755-758. doi: 10.1109/IS3C.2016.193. LI Y B, ZHANG F, KANG X J, at al. Image encryption based on the iterative fractional Fourier transform and a novel pixel scrambling technique[C]. IET International Radar Conference, Hangzhou, 2015: 1-6. doi: 10.1049/cp.2015.1036. 冉啟文. 小波變換與分數(shù)階傅里葉變換理論及應用[M]. 哈爾濱: 哈爾濱工業(yè)大學出版社, 2001: 1-7. RAN Qiwen. Theory and Application of Wavelet Transform and Fractional Fourier Transform[M]. Harbin: Harbin Institute of Technology Press, 2001: 1-7. GUAN J, CHEN X L, HUANG Y, at al. Adaptive fractional Fourier transform based detection algorithm for moving target in heavy sea clutter[J]. IET Radar, Sonar Navigation, 2012, 6(5): 389-401. doi: 10.1049/iet-rsn.2011. 0030. 王亞星. 基于分數(shù)階傅里葉變換的人臉識別[D]. [碩士論文], 鄭州大學, 2015 WANG Yaxing. Human facial expression recognition based on fractional Fourier transform[D]. [Master dissertation], Zhengzhou University, 2015. OZAKTAS H M, ARIKAN O, KUTAY M A, et al. Digital computation of the fractional Fourier transform[J]. IEEE Transactons on Signal Processing, 1996, 44(9): 2141-2150. 李志鵬, 馬田香, 杜蘭, 等. 在雷達HRRP識別中多特征融合多類分類器設計[J]. 西安電子科技大學學報, 2013, 40(1): 111-117. doi: 10.3969/j.issn.1001-2400.2013.01.020. LI Zhipeng, MA Tianxiang, DU Lan, et al. Multi-class classifier design for feature fusion in radar HRRP recognition [J]. Journal of Xidian University , 2013, 40(1): 111-117. doi: 10.3969/j.issn.1001-2400.2013.01.020. 王寶帥, 杜蘭, 劉宏偉. 基于經(jīng)驗模態(tài)分解的空中飛機目標分類[J]. 電子與信息學報, 2012, 34(9): 2116-2121. doi: 10. 3724/SP.J.1146.2012.00147. WANG Baoshuai, DU Lan, and LIU Hongwei. Aircraft classification based on empirical mode decomposition[J]. Journal of Electronics Information Technology, 2012, 34(9): 2116-2121. doi: 10.3724/SP.J.1146.2012.00147. MARTIN J and MULGREW B. Analysis of the theoretical radar return signal from aircraft propeller blades[C]. IEEE International Conference Radar, New York, USA, 1990: 569-572. BELL M R and GRUBBS R A. JEM modeling and measurement for radar target identification[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 73-87. doi: 10.1109/7.249114. -
計量
- 文章訪問數(shù): 1676
- HTML全文瀏覽量: 135
- PDF下載量: 303
- 被引次數(shù): 0
下載:
