Volume 42 Issue 1
Feb.  2014
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Zhu Feng, Zhang Qun, Feng Youqian, Luo Ying, Li Kaiming, Liang Bishuai. Compressed sensing identification approach for avian with inverse synthetic aperture lidar[J]. Infrared and Laser Engineering, 2013, 42(1): 256-261.
Citation: Zhu Feng, Zhang Qun, Feng Youqian, Luo Ying, Li Kaiming, Liang Bishuai. Compressed sensing identification approach for avian with inverse synthetic aperture lidar[J]. Infrared and Laser Engineering, 2013, 42(1): 256-261.

Compressed sensing identification approach for avian with inverse synthetic aperture lidar

  • Received Date: 2012-05-22
  • Rev Recd Date: 2012-06-19
  • Publish Date: 2013-01-25
  • It is very significant to detect avian in a real time and identify them exactly. A novel approach of avian detection, imaging and identification was proposed in this paper with inverse synthetic aperture lidar(ISAIL) based on compressed sensing. The proposed approach can be stated as follows. Firstly, the optical heterodyne method and compressed sensing sampling were employed orderly to diminish sampling rate of the avian ISAIL echoes in the range. Secondly, the time-frequency analysis technique was engaged to discriminate the different moving statuses of the bird. What's more, the compressed sensing reconstruction algorithm was utilized to obtain the high resolution two-dimensional image of the bird and the fitting algorithm was used to extract the micro-Doppler feature of the bird. The avian identification and recognition can be executed based on the reconstructed high resolution two-dimensional image and the extracted micro-Doppler feature of the bird. The effectiveness of the proposed approach is validated by the simulation results.
  • [1] Feng C, Zhu F, Li S. Avian micro-Doppler feature extraction based on frequency-stepped chirp ISAR [C]//The International Conference on Signal and Image Processing, 2010: 1-4.
    [2]
    [3] Li Fan, Wu Shuangyang, Zheng Yongchao, et al. Overview of the development of synthetic aperture lidar [J]. Infrared and Laser Engineering, 2006, 35(1): 55-65. (in Chinese)
    [4]
    [5]
    [6] Xing Mengdao, Guo Liang, Tang Yu, et al. Design on the experiment optical system of synthetic aperture imaging lidar [J]. Infrared and Laser Engineering, 2009, 38(2): 290-294. (in Chinese)
    [7] Zhang Wenrui, Zeng Xiaodong, Man Xiangkun. Study on optical heterodyne detection [J]. Infrared and Laser Engineering, 2008, 37(2): 146-147. (in Chinese)
    [8]
    [9] Donoho D. Compressed sensing [J]. IEEE Transactions Information Theory, 2006, 52(4): 1289-1306.
    [10]
    [11] Liang Hong, Hu Xujuan, Zhu Yunzhou. Multi-component LFM signal detection based on reassign-smooth-pseudo-Wigner-Ville distribution [J]. Journal of System Simulation, 2007, 19(13): 3030-3033. (in Chinese)
    [12]
    [13] Jeong H, Kim H, Kim H. Application of subarray averaging and entropy minimization algorithm to stepped frequency ISAR autofocus [J]. IEEE Transactions on Antennas and Propagation, 2008, 56(4): 1144-1154.
    [14]
    [15] Candes E, Tao T. Near optimal signal recovery from random projections: universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425.
    [16]
    [17] Mohimani H, Babaie-Zadeh M, Jutten C. A fast approach for overcomplete sparse decomposition based on smoothed l0 norm [J]. IEEE Transactions on Signal Processing, 2009, 57(1): 289-301.
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Compressed sensing identification approach for avian with inverse synthetic aperture lidar

  • 1. Institute of Information and Navigation,Air Force Engineering University,Xi'an 710077,China;
  • 2. No.93508 Unit of PLA,Beijing 100079,China;
  • 3. Institute of Science,Air Force Engineering University,Xi'an 710051,China

Abstract: It is very significant to detect avian in a real time and identify them exactly. A novel approach of avian detection, imaging and identification was proposed in this paper with inverse synthetic aperture lidar(ISAIL) based on compressed sensing. The proposed approach can be stated as follows. Firstly, the optical heterodyne method and compressed sensing sampling were employed orderly to diminish sampling rate of the avian ISAIL echoes in the range. Secondly, the time-frequency analysis technique was engaged to discriminate the different moving statuses of the bird. What's more, the compressed sensing reconstruction algorithm was utilized to obtain the high resolution two-dimensional image of the bird and the fitting algorithm was used to extract the micro-Doppler feature of the bird. The avian identification and recognition can be executed based on the reconstructed high resolution two-dimensional image and the extracted micro-Doppler feature of the bird. The effectiveness of the proposed approach is validated by the simulation results.

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