Background Classification Method for Marine Target Detection Based on CNN

被引：0
|
作者：
Xu Y.-N. [1 ]
Liu N.-B. [1 ]
Ding H. [1 ]
Guan J. [1 ]
Huang Y. [1 ]
机构：
[1] Information Fusion Institute, Naval Aviation University, Yantai, 264001, Shandong
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2019年 / 47卷 / 12期
关键词：
Classification; Convolution neural network; Detection background; Sea clutter;
D O I：
10.3969/j.issn.0372-2112.2019.12.008
中图分类号：
学科分类号：
摘要：
In this paper, the background classification method of marine target detection based on convolutional neural network (CNN) is mainly studied.Taking LeNet as an example, based on the IPIX measured data set, the model training through controlling variables is carried out.The feasibility of using CNN in the classification of sea clutter and noise in one dimensional radar echo signal is studied, and the influence of factors such as data preprocessing, single sample sequence length and network structure parameters on classification accuracy is analyzed synchronously, and verified for the typical detection scene classification.The application results of measured data show that the proposed method has high accuracy in clutter classification and noise classification under the conditions of forward/reverse direction and high/low sea conditions. © 2019, Chinese Institute of Electronics. All right reserved.
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页码：2505 / 2514
页数：9
相关论文
共 20 条
  • [1] Ding H., Dong Y.-L., Liu N.-B., Et al., Overview and prospects of research on sea clutter property cognition, Journal of Radars, 5, 5, pp. 499-516, (2016)
  • [2] Liu N.-B., Guan J., Wang G.-Q., Et al., Target detection within sea clutter based on multi-scale hurst exponent in frft domain, Acta Electronica Sinica, 41, 9, pp. 1847-1853, (2013)
  • [3] Yin Z.-Y., Zhang Y.-S., Radar sea clutter modeling of statistical characteristic, Equipment Environmental Engineering, 14, 7, pp. 29-34, (2017)
  • [4] Tugnait J.K., Two-channel tests for common non-Gaussian signal detection, IEEE Proceedings of Radar and Signal Porcessing, 140, 6, pp. 343-349, (1993)
  • [5] Pournejatian N.M., Nayebi M.M., Fractal-multiresolution based detection of targets within sea clutter, Electronics Letters, 48, 6, (2012)
  • [6] Shui P.L., Li D.C., Xu S.W., Tri-feature-based detection of floating small targets in sea clutter, IEEE Transactions on Aerospace and Electronic Systems, 50, 2, pp. 1416-1430, (2014)
  • [7] Darzikolaei M.A., Ebrahimzade A.A., Gholami E., Classification of radar clutters with artificial neural network, International Conference on Knowledge-Based Engineering and Innovation, pp. 577-581, (2015)
  • [8] Vicen-Bueno, Raul, Carrasco-Alvarez, Et al., Sea clutter reduction and target enhancement by neural networks in a marine radar system, Sensors, 9, 3, pp. 1913-1936, (2009)
  • [9] Callaghan D., Burger J., Mishra A.K., A machine learning approach to radar sea clutter suppression, Radar Conference, pp. 1222-1227, (2017)
  • [10] Shi S.N., Shui P.L., Sea-surface floating small target detection by one-class classifier in time-frequency feature space, IEEE Transactions on Geoscience & Remote Sensing, 56, 11, pp. 6395-6411, (2018)
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