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一種利用相位信息的雷達(dá)目標(biāo)成像識(shí)別方法

劉本永,  楊萬(wàn)麟

劉本永, 楊萬(wàn)麟. 一種利用相位信息的雷達(dá)目標(biāo)成像識(shí)別方法[J]. 電子與信息學(xué)報(bào), 2000, 22(2): 274-278.
引用本文: 劉本永, 楊萬(wàn)麟. 一種利用相位信息的雷達(dá)目標(biāo)成像識(shí)別方法[J]. 電子與信息學(xué)報(bào), 2000, 22(2): 274-278.
Liu Benyong, Yang Wanlin. IMAGE RECOGNITION OF RADAR TARGET USING PHASE INFORMATION[J]. Journal of Electronics & Information Technology, 2000, 22(2): 274-278.
Citation: Liu Benyong, Yang Wanlin. IMAGE RECOGNITION OF RADAR TARGET USING PHASE INFORMATION[J]. Journal of Electronics & Information Technology, 2000, 22(2): 274-278.

一種利用相位信息的雷達(dá)目標(biāo)成像識(shí)別方法

IMAGE RECOGNITION OF RADAR TARGET USING PHASE INFORMATION

  • 摘要: 高距離分辨雷達(dá)(HRR)的回波中蘊(yùn)含豐富的目標(biāo)信息,充分利用目標(biāo)回波并采用恰當(dāng)?shù)奶卣鞒槿》椒?可以有效地識(shí)別目標(biāo)。本文提出一種利用相位信息和正則變換的目標(biāo)識(shí)別方法。該方法基于雷達(dá)目標(biāo)距離剖面像的幅度和相位矢量(幅相矢量),首先由各訓(xùn)練目標(biāo)在不同方位姿態(tài)角時(shí)的幅相矢量構(gòu)成綜合矩陣并對(duì)之作正則變換建立正則子空間,然后將每類訓(xùn)練目標(biāo)各方位姿態(tài)的幅相矢量向該子空間投影形成子像,取其平均結(jié)果作為該目標(biāo)的庫(kù)特征矢量。對(duì)未知目標(biāo),以其子像對(duì)庫(kù)矢量的歐氏距離最小為分類準(zhǔn)則,進(jìn)行了識(shí)別模擬實(shí)驗(yàn)。
    Abstract: The echo signal of a high-range-resolution radar(HRR) contains much information about target. By using this information and feature extraction method fully and properly, an efficient target recognition can be executed. A novel approach for radar target recognition is proposed in this paper. This approach explores canonical analysis on a matrix formed by the image vectors of training targets in different aspect angles. One image vector contains both the amplitudes and relative phases of the range profile of a target. A subspace is obtained from this analysis. Projection of an image vector into this subspace forms subimage. The subimages of a training target in different aspect angles are averaged into library feature vector for this target. Using the subimage of an unknown target as feature vector and minimum distance rule for target recognition, experiments on simulated data are done.
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  • Li H J, Yang S H. Using range profiles as feature vectors to identify aerospace objects. IEEE Trans. on AP, 1993, AP-41(3): 261-265.[2]何松華,孫文峰,郭桂蓉.基于光學(xué)區(qū)雷達(dá)目標(biāo)頻率響應(yīng)的結(jié)構(gòu)成像與目標(biāo)識(shí)別的研究.電子學(xué)報(bào),1996,24(3):32-36.[3]Watanabe S, Pakvasa N. Subspace method of pattern recognition. Proc.lst. Int.Conf. on Pattern Recognition, Washington, DC: 1973, 25-32.[4]E.奧亞著,蔡國(guó)廉,揚(yáng)文瑜譯.子空間法模式識(shí)別.北京:科學(xué)出版社,1987,1-4.[5]Sirovich L, Kirby M. Low-dimensional procedure for the characterization of human faces[J].J. Opt.Soc. Am. A.1987, 4(3):519-524[6]Turk M A, Pentland A P. Face recognition using eigenfaces. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Lahaina, Maui, Hawaii: 1991, 586-591.[7]Krzanowski W J. Principles of Multivariate Analssis: A Users Perspective. Oxford Clarenton Press, 1988, 291-306.[8]Novak L M, Owirka G J. Radar target identification using an eigen-image approach. IEEE International Radar Conference, Atlanta, Georgia: 1994, 129-131.[9]Belhumeur P N, Hespanha J P, Kriegman D K. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997,PAMI-19(7): 711-720.[10]Oja E, Parkkinen J. Texture Spaces. in Devijver P A, Kittler J eds., Pattern Recognition Theory and Application, Berlin, Heidelberg: Springer-Verlag, 1987, 21-33.[11]黃德雙,韓月秋.基于位置相關(guān)的高分辨雷達(dá)目標(biāo)檢測(cè)方法.電子科學(xué)學(xué)刊,1997,19(5):584-590.[12]Wehner D R. High Resolution Radar. Norwood: Artech House, Inc., 1987, 77-79.[13]陳大慶,保錚.基于多層前向網(wǎng)絡(luò)的雷達(dá)目標(biāo)一維距離像識(shí)別.西安電子科技大學(xué)學(xué)報(bào),1997,24(1):1-7.
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出版歷程
  • 收稿日期:  1998-06-22
  • 修回日期:  1999-02-12
  • 刊出日期:  2000-03-19

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