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Volume 38 Issue 5
May  2016
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ZHANG Lin, LI Xiuyou, LIU Ningbo, GUAN Jian. Improved EMD Target Detection Method Based on Mono Fractal Characteristics[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1041-1046. doi: 10.11999/JEIT150731
Citation: ZHANG Lin, LI Xiuyou, LIU Ningbo, GUAN Jian. Improved EMD Target Detection Method Based on Mono Fractal Characteristics[J]. Journal of Electronics & Information Technology, 2016, 38(5): 1041-1046. doi: 10.11999/JEIT150731

Improved EMD Target Detection Method Based on Mono Fractal Characteristics

doi: 10.11999/JEIT150731
Funds:

The National Natural Science Foundation of China (61501487, 61471382, 61401495, 61201445, 61179017), The Natural Science Foundation of Shandong Province (2015ZRA 06052), The Special Funds of Taishan Scholars Construction Engineering

  • Received Date: 2015-06-15
  • Rev Recd Date: 2016-01-29
  • Publish Date: 2016-05-19
  • In order to overcome the detection performance degradation of the existing detection method when the target and sea clutter is hard to distinguish, an improved target detection method based on mono fractal characteristics is proposed. Firstly, for getting the Intrinsic Mode Function (IMF) after reconstruction, the original signal is decomposed by using Empirical Mode Decomposition (EMD), then the spectrum of target bin and sea clutter bin after denoising is gained by using Fast Fourier Transform (FFT), Mono-Hurst exponents are calculated and the target is detected by nonparametric detector. The results show that, although target and sea clutter is hard to distinguish from frequency spectrum, but their Mono-Hurst exponents is different in scale-invariant interval, compared with original detection method in frequency domain, the proposed method can achieve good detection performance.
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  • HUANG N E, SHEN Zheng, LONG S R, et al. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society, 1998, 454: 903-995.
    YANG Gongliu, LIU Yuanyuan, WANG Yanyong, et al. EMD interval thresholding denoising based on similarity measure to select relevant modes[J]. Signal Processing, 2015, 109: 95-109.
    AHMET Mert and AVDIN Akan. Detrended fluctuation thresholding for empirical mode decomposition based denoising[J]. Digital Signal Processing, 2014, 32: 48-56.
    FAIRCHILD D P and NARAYANAN R M. Classification of human motions using empirical mode decomposition of human micro-Doppler signatures[J]. IET Radar, Sonar Navigation, 2014, 8(5): 425-434.
    YUAN Bin, CHEN Zengping, and XU Shiyou. Micro- Doppler analysis and separation based on complex local mean decomposition for aircraft with fast-rotating parts in ISAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1285-1298.
    SONG Rui, GUO Huadong, and LIU Guang. Improved Goldstein SAR Interferogram filter based on empirical mode decomposition[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(2): 399-403.
    林文晶, 张榆锋, 章克信, 等. 总体经验模态细分法提取血流超声多普勒信号的研究[J]. 电子学报, 2014, 42(7): 1424-1428.
    LIN Wenjing, ZHANG Yufeng, ZHANG Kexin, et al. Extraction of Doppler ultrasound blood signals using the delicate separation method based on the EEMD algorithm [J]. Acta Electronica Sinica, 2014, 42(7): 1424-1428.
    余炜, 周娅, 马晶晶, 等. 基于EMD和LVQ的信号特征提取及分类方法[J]. 数据采集与处理, 2014, 29(5): 683-688.
    YU Wei, ZHOU Ya, MA Jingjing, et al. Signal feature extraction and classification method based on EMD and LVQ neural network[J]. Journal of Data Acquisition and Processing, 2014, 29(5): 683-688.
    王玉静, 康守强, 张云, 等. 基于集合经验模态分解敏感固有模态函数选择算法的滚动轴承状态识别方法[J]. 电子与信息学报, 2014, 36(3): 595-600. doi: 10.3724/SP.J.1146.2013. 00434.
    WANG Yujing, KANG Shouqiang, ZHANG Yun, et al. Condition recognition method of rolling bearing based on ensemble empirical mode decomposition sensitive intrinsic mode function selection algorithm[J]. Journal of Electronics Information Technology, 2014, 36(3): 595-600. doi: 10.3724/SP.J.1146.2013.00434.
    肖春生, 察豪, 周沫. 基于EMD的海杂波特性与目标检测[J]. 雷达与对抗, 2011, 31(2): 1-4.
    XIAO Chunsheng, CHA Hao, and ZHOU Mo. The EMD- based sea clutter characteristics and target detection[J]. Radar ECM, 2011, 31(2): 1-4.
    王幅友, 刘刚, 袁赣南. 基于EMD算法的海杂波信号去噪[J]. 雷达科学与技术, 2010, 8(2): 177-182.
    WANG Fuyou, LIU Gang, and YUAN Gannan. EMD-based sea clutter signal denoising[J]. Radar Science and Technology, 2010, 8(2): 177-182.
    关键, 张建. 基于固有模态能量熵的微弱目标检测算法[J]. 电子与信息学报, 2011, 33(10): 2494-2499. doi: 10.3724/SP.J. 1146.2011.00147.
    GUAN Jian and ZHANG Jian. Weak target detection based on intrinsic mode energy entropy[J]. Journal of Electronics Information Technology, 2011, 33(10): 2494-2499. doi: 10.3724/SP.J.1146.2011.00147.
    刘宁波, 关键, 宋杰, 等. 海杂波频谱的多重分形特性分析[J]. 中国科学:信息科学, 2013, 43(6): 768-782.
    LIU Ningbo, GUAN Jian, SONG Jie, et al. Multifractal property of sea clutter frequency spectrum[J]. Scientia Sinica Informationis, 2013, 43(6): 768-783.
    HU Jing, TUNG Wenwen, and GAO Jianbo. 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.
    HANSEN V G. Nonparametric radar extraction using a generalized sign test[J]. IEEE Transactions on Aerospace and Electronic Systems, 1971, 7(5): 942-950.
    SALMASI M and MODARRES-HASHEMI M. Design and analysis of fractal detector for high resolution radars[J]. Chaos, Solitons Fractals, 2009, 40(5): 2133-2145.
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