• Infrared and Laser Engineering
  • Vol. 47, Issue 8, 826005 (2018)
Zhao Dongbo1、2、* and Li Hui2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3788/irla201847.0826005 Cite this Article
    Zhao Dongbo, Li Hui. Radar target recognition based on central moment feature and GA-BP neural network[J]. Infrared and Laser Engineering, 2018, 47(8): 826005 Copy Citation Text show less
    References

    [1] Xu Bin, Chen Bohai, Liu Hongwei, et al. Based on the recurrent neural network model, radar high resolution distance image target recognition [J]. Journal of Electronics and Information, 2016, 38(12): 2988-2995. (in Chinese)

    [2] Wu Zhanjun, Niu Min, Xu Bing, et al. Research on recognition method based on spectral regression feature reduction and backward propagation neural network[J]. Journal of Electronic and Information, 2016, 38(4): 978-984. (in Chinese)

    [3] Li Haipeng, Li Jingjiao, Yan Aiyun, et al. The parallel realization of genetic neural network in face recognition[J]. Computer Science, 2015, 42(6A): 168-174. (in Chinese)

    [4] Yang Xufeng, Lin Wei, Yan Weidong, et al. SAR image target recognition using thermonuclear characteristics [J]. Infrared and Laser Engineering, 2014, 43(11): 3794-3801. (in Chinese)

    [5] Li Hui, Jin Baolong, Zhai Haitian. High resolution radar signal translation invariant KPCA feature extraction algorithm [J]. Computer Simulation, 2012, 29(1): 9-12. (in Chinese)

    [6] Sun Shaoyuan, Li Linna, Zhao Haitao. Depth estimation of monocular vehicle infrared images using KPCA and BP neural networks [J]. Infrared and Laser Engineering, 2013, 42(9): 2348-2352. (in Chinese)

    [7] Yuan Pu, Mao Jianlin, Xiang Fenghong, et al. Improved network fault diagnosis based on genetic optimization BP neural network [J]. Power System and Automation Journal, 2017, 29(1): 118-122. (in Chinese)

    [8] Liu Yanju, Kou Guohao, Song Jianhui. Air target recognition technology based on RBF neural network[J]. Fire and Command Control, 2015, 40(8): 9-13. (in Chinese)

    [9] Cheng Liang, Zhu Li. Millimeter wave radiometer target recognition based on PCA optimized radial basis function neural network [J]. Acta Microwave Sinica, 2015, 10: 225-229. (in Chinese)

    [10] Yang Wenxiu, Fu Wenxing, Zhou Zhiwei, et al. Lidar rapid target recognition based on projection dimension reduction [J]. Infrared and Laser Engineering, 2014, 43(S): 0001-0007. (in Chinese)

    [11] Qin Guohua, Xie Wenbin, Wang Huamin. Tool wear detection and control based on neural network and genetic algorithm [J]. Optics and Precision Engineering, 2015, 23(5): 1314-1321. (in Chinese)

    [12] Nie Haitao, Long Kehui, Ma Jun, et al. Fast object recognition under multiple varying background using improved SIFT method[J]. Optics and Precision Engineering, 2015, 23(8): 2349-2356. (in Chinese)

    [13] Xiao Yongsheng, Huang Lizhen, Zhou Jianjiang. RATR of adaptive angular-sector segmentation based on grey incidence analysis model[J]. Grey Systems: Theory and Application, 2017, 7(1): 71-79.

    [14] Cao Wei, Zhou Hui, Zhou Zhimin, et al. An approach for high resolution radar target recognition based on BP neural network[C]//International Conference on Intelligent Computing, ICIC 2011: Advanced Intelligent Computing, 2011: 33-39.

    [15] Zhou Daiying. Radar target HRRP recognition based on reconstructive and discriminative dictionary learning[J]. Signal Processing, 2016, 126(11): 52-64.

    [16] Huang Xiayuan, Nie Xiangli, Hong Weiwua, et al. SAR target configuration recognition based on the biologically inspired model[J]. Neurocomputing, 2017, 234(4): 185-191.

    Zhao Dongbo, Li Hui. Radar target recognition based on central moment feature and GA-BP neural network[J]. Infrared and Laser Engineering, 2018, 47(8): 826005
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