期刊文献+

海杂波FRFT域分形特征判别及动目标检测方法 被引量:23

Fractal Feature Discriminant of Sea Clutter in FRFT Domain and Moving Target Detection Algorithm
下载PDF
导出
摘要 该文研究了海杂波在分数阶Fourier变换(FRFT)域的分形特征,提出了一种基于分形特征差异的联合动目标检测方法。首先,分析了海杂波数据在FRFT域的统计特性,通过对不同极化方式下分形曲线的仿真分析,得到海杂波在FRFT域满足自相似性。其次,给出了分形参数的提取方法和无标度区间,并分析了变换阶数对分形参数估计的影响。最后,利用临近距离单元或临近时刻的雷达回波信号在FRFT域的分形维数和斜距的差值作为检测统计量,经不同极化方式下的海杂波数据验证,表明算法不仅具有良好的微弱动目标检测能力,而且能够准确估计目标的运动状态。 A new moving target detection algorithm is proposed based on the joint fractal properties discriminant of sea clutter in FRactional Fourier Transform(FRFT) domain.At first,statistical characteristic of sea clutter data in FRFT domain is analyzed and simulations of fractal curves in different polarizations are conducted,which indicates the self-similarity feature.Then,determination method of fractal parameters and scale-invariant interval is given and influence of transform order on the estimation of fractal parameters is also discussed.Finally,differences of fractal dimension and intercept in FRFT domain,which are calculated from adjacent range bin or time series of radar echo,can be used as test statistic.Real sea clutter in different polarizations is used for verification and the results present that the proposed algorithm has good performance for weak moving target detection and can also give high estimation accuracy of moving conditions.
出处 《电子与信息学报》 EI CSCD 北大核心 2011年第4期823-830,共8页 Journal of Electronics & Information Technology
基金 国家自然科学基金(60672140 60802088) 航空科学基金(20095184004) "泰山学者"建设工程专项经费资助课题
关键词 动目标检测 海杂波 分数阶Fourier变换(FRFT) 分形维数 斜距 Moving target detection Sea clutter FRactional Fourier Transform(FRFT) Fractal dimension Intercept
  • 引文网络
  • 相关文献

参考文献11

  • 1Carretero-Moya 3, Gismero-Menoyo J, and Asensio-Ldpez A, et al.. Application of the Radon transform to detect smalltargets in sea clutter[J]. IET Radar, Sonar and Navigation, 2009, 3(2): 155-166.
  • 2Younsi A, Greco M, Gini F, and Zoubir A M. Performance of the adaptive generalized matched subspace constant false alarm rate detector in non-Gaussian noise: an experimental analysis[J]. IET Radar, Sonar and Navigation, 2009, 3(3): 195-202.
  • 3Hu J, Tung W W, and Gao J B. Detection of low observable targets within sea clutter by structure function based multifractal analysis[J]. IEEE Transactions on Antennas and Propagation, 2006, 54(1): 136-143.
  • 4Sun Hong-bo, Liu Guo-sui, and Gu Hong. Application of the fractional Fourier transform to moving target detection in airborne SAR[J]. IEEE Transactions on Aerospace and Electronic Systems, 2002, 38(4): 1416-1424.
  • 5Lv Xiaolei, Xing Meng-dao, and Zhang Shou-hong, et al.. Keystone transformation of the Wigner-Ville distribution for analysis of multicomponent LFM signals[J]. Signal Processing (Elsevier), 2009, 89(5): 791-806.
  • 6Ltltfiye Durak and Orhan Ar-kan. Short-time Fourier transform two fundamental properties and an optimal implementation[J]. IEEE Transactions on Signal Processing, 2003, 51(5): 1231-1242.
  • 7张南,陶然,王越.基于变标处理和分数阶傅里叶变换的运动目标检测算法[J].电子学报,2010,38(3):683-688. 被引量:16
  • 8Chen Yang-quan, Sun Rong-tao, and Zhou An-hong. An improved Hurst parameter estimator based on fractional Fourier transform[C]. Proceedings of the ASME 2007 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Las Vegas, Nevada, USA, 2007: 1-11.
  • 9李宝,关键,刘宁波.海杂波FRFT域的分形特性及目标检测[J].雷达科学与技术,2009,7(3):210-213. 被引量:6
  • 10Drosopoulos A. Description of the OHGR Database[R}. Technology Note No. 94-14, Ottawa: Defence Research Establishment, 1994: 1-30.

二级参考文献25

共引文献35

同被引文献220

引证文献23

二级引证文献134

;
使用帮助 返回顶部