寬帶雷達光學區(qū)頻域識別法
FREQUENCY-DOMAIN RECOGNITION METHOD FOR WIDEBAND RADAR OPTICAL REGION TARGET
-
摘要: 該文以寬帶雷達光學區(qū)目標識別為背景,由頻域測量數(shù)據(jù)構造了不隨目標距離像沿徑向平移而改變的頻域波形回波幅值波形和相位特征波形;基于此波形,提取了兩種對目標方位角不敏感的識別特征廣義頻數(shù)和波形長度;并借助于時頻分析中尺度變換的概念,把特征集進一步完備化。針對頻域直接識別法易受測量噪聲影響的缺點,設計了相應的預處理算法。選用FMM神經(jīng)網(wǎng)絡作為分類器,并修改了它傳統(tǒng)的學習算法。對5種噴氣飛機模型的識別結果表明,該算法具有較高的正確識別率。Abstract: Meeting the application requirements of wideband radar optical region target recognition, this paper presents a simple and effective frequency-domain recognition method. First, two kinds of waves called backscattering amplitude wave and phase feature wave are constructed directly from frequency measured data sets, which keep invariant on the shift of target in the radial direction. Based on these waves, generalized frequency and length of wave are extracted as recognition features insensitive to target azimuth. With the aid of the idea of ruler transform in time-frequency analysis, the feature sets are further completed. Aiming at lessening the effect of measuring noise, the paper then designs a specific preprocessing method. FMM neural network is chosen as the classifier with modified training algorithm. The recog- nition results show that this target recognition algorithm can obtain high correct classification rate.
-
W.M. Steedly, R. L. Moses, High resolution exponential modeling of fully polarized radar returns,IEEE Trans. on AES, 1991, 27(3), 459-468.[2]王雪松,莊釗文,肖順平等,光學區(qū)雷達目標空間極化結構特性描述及識別研究,電子學報,1998,26(6),36-41.[3]肖順平,郭桂蓉,莊釗文等,基于含最小二乘估計曲線擬合的極化雷達目標識別方法,電子學報,1997,25(3),32-36.[4]王雪松,肖順平,莊釗文,基于改進退火法擬合參數(shù)估計的極化雷達目標識別,現(xiàn)代雷達,1997,19(2),6-11.[5]肖順平,莊釗文,王雪松等,目標動態(tài)極化結構特征提取與識別,電子學報,1998,26(3),48-52.[6]肖順平,王雪松,莊釗文,基于極化不變量的飛機目標識別,紅外與毫米波學報,1996,15(6),439-444[7]肖順平,王雪松,郭桂蓉等,基于極化域能量譜的飛機目標識別,宇航學報,1998,19(3),23-28.[8]肖懷鐵,莊釗文,郭桂蓉,基于遞歸神經(jīng)網(wǎng)絡的飛機目標識別方法,國防科技大學學報,1997,19(4),48 53.[9]A. Zyweck, R. E. Bogner, Radar target classification of commercial aircraft, IEEE Trans. on AES, 1996, 32(2), 598-660.[10]R. Bhatnagar, R. Horvitz, R. Williams, A hybrid system for target classification, Patt. Recog.Lett., 1997, 18, 1399-1403.[11]J. S. Chen, E. K. Walton, Comparison of two target classification techniques, IEEE Trans. on AES, 1986, 22(1), 15-21.[12]P. K. Simpson, Fuzzy min-max neural network-Part 1: classification, IEEE Trans. on Neural Networks, 1992, 3(5), 776-786. -
計量
- 文章訪問數(shù): 2561
- HTML全文瀏覽量: 114
- PDF下載量: 814
- 被引次數(shù): 0
下載:
