A Hierarchical FOD Detection Method Based on Eigenvalue Spectrum Features

WANG Baoshuai; LIU Jianghong; ZHENG Xiaoliang; HE Minjue; XIAO Qing

doi:10.11999/JEIT170178

Volume 39 Issue 11

Nov. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(11): 2690-2696

WANG Baoshuai, LIU Jianghong, ZHENG Xiaoliang, HE Minjue, XIAO Qing. A Hierarchical FOD Detection Method Based on Eigenvalue Spectrum Features[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2690-2696. doi: 10.11999/JEIT170178

Citation:

WANG Baoshuai, LIU Jianghong, ZHENG Xiaoliang, HE Minjue, XIAO Qing. A Hierarchical FOD Detection Method Based on Eigenvalue Spectrum Features[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2690-2696. doi: 10.11999/JEIT170178

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A Hierarchical FOD Detection Method Based on Eigenvalue Spectrum Features

doi: 10.11999/JEIT170178

Received Date: 2017-03-01
Rev Recd Date: 2017-08-25
Publish Date: 2017-11-19

Abstract

Abstract

Detection of weak targets in heavy ground clutter is the key issue for Foreign Object Debris (FOD) surveillance radar on airport runways. A novel hierarchical FOD detection method is proposed based on eigenvalue spectrum feature extraction and Minimax Probability Machine (MPM). The clutter map Constant False Alarm Rate (CFAR) detection algorithm is utilized firstly to categorize radar echoes into two kinds, i.e., background clutter and the FOD returns (including the false alarm returns). Then eigenvalue spectrum features are extracted to transform the FOD returns and false alarm returns into the feature domain where the FOD and false alarm are more distinguishable. Finally, the MPM classifier is utilized to categorize the FOD and false alarm into different kinds so as to reduce the false alarm rate. Experiments results based on measured data show that the proposed method can achieve good detection performance.
- Millimeter wave radar,
- Airport runway Foreign Object Debris (FOD),
- Feature extraction,
- Minimax Probability Machine (MPM)

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References(33)

References

张思睿, 葛万成, 汪亮友. 恶劣天气下可见光和红外图像融合算法设计[J]. 信息技术, 2016, 6(1): 33-36. doi: 10.13274/j. cnki.hdzj.2016.06.010.

MAZOUNI K, Kohmura A, Futasumori S, et al. 77 GHz FMCW radar for FODs detection[C]. Proceeding of the 77th European Radar Conference. Paris, France, 2010: 451-454.

ZHANG Sirui, GE Wancheng, and WANG Liangyou. Design of the image fusion algorithm with infrared image and visible image under severe weather conditions[J]. Information Technology, 2016, 6(1): 33-36. doi: 10.13274/j.cnki.hdzj.2016. 06.010.

李华琼, 张中仅, 王雨果, 等. CFAR方法在机场跑道FOD检测中的性能分析[J]. 无线电工程, 2015, 45(9): 53-57. doi: 10.3969/j.issn.1003-3106.2015.09.14.

LI Huaqiong, ZHANG Zhongjin, WANG Yuguo, et al. Performance analysis and comparison of CFAR methods for FOD detection in airport runway environment[J]. Radio Engineering, 2015, 45(9): 53-57. doi: 10.3969/j.issn.1003- 3106.2015.09.14.

ANAS Tom and VISWANATHAN R. Switched order statistics CFAR test for target detection[C]. 2008 IEEE Radar Conference, Rome, Italy, 2008: 1-5.

ZATTONTA B, FARROUKI A, and BARKAT M. Automatic censoring detection using binary clutter-map estimation for non-Gaussian enviroments[C]. IEEE International Conference on Signal Processing and Communications, Dubai, United Arab Emirates, 2007: 205-208.

NITZBERG R. Clutter map CFAR analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22(4): 419-421.

CONTE E and LONGO M. Modelling and simulation of non-Rayleigh radar clutter[J]. IEE Proceeding of Radar and Signal Processing, 1991, 138(2): 121-130.

SCHLEHER D C. Radar detection in Weibull clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1976, 12(6): 736-743.

张群, 何其芳, 罗迎. 基于贝塞尔函数基信号分解的微动群目标特征提取方法[J]. 电子与信息学报, 2016, 38(12): 3056-3062. doi: 10.11999/JEIT161036.

ZHANG Qun, HE Qifang, and LUO Ying. Micro-Doppler feature extraction of group targets using signal decomposition based on Bessel function basis[J]. Journal of Electronics Information Technology, 2016, 38(12): 3056-3062. doi: 10.11999/JEIT161036.

杜兰, 史蕙若, 李林森, 等. 基于分数阶傅立叶变换的窄带雷达飞机目标回波特征提取方法[J]. 电子与信息学报, 2016, 38(12): 3093-3099. doi: 10.11999/JEIT161035.

DU Lan, SHI Huiruo, LI Linsen, et al. Feature extraction method of narrow-band radar airplane signatures based on fractional Fourier transform[J]. Journal of Electronics Information Technology, 2016, 38(12): 3093-3099. doi: 10.11999/JEIT161035.

DU Lan, WANG Baoshuai, and WANG Penghui. Noise reduction method based on principal component analysis with Beta process for micro-Doppler radar signatures[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 4028-4040.

NANZER J A and ROGERS R L. Bayesian classification of humans and vehicles using micro-Doppler signals from a scanning-beam radar[J]. IEEE Microwave Wireless Component Letter, 2009, 19(5): 338-340.

李彦兵, 杜兰, 刘宏伟, 等. 基于微多普勒特征的地面目标分类[J]. 电子与信息学报, 2010, 32(12): 2848-2853. doi: 10.3724 /SP.J.1146.2010.00128.

LI Yanbing, DU Lan, LIU Hongwei, et al. Ground targets classification based on micro-Doppler effect[J]. Journal of Electronics Information Technology, 2010, 32(12): 2848-2853. doi: 10.3724/SP.J.1146.2010.00128.

杜兰, 刘彬, 王燕, 等. 基于卷积神经网络的SAR图像目标检测算法[J]. 电子与信息学报, 2016, 38(12): 3018-3025. doi: 10.11999/JEIT161032.

DU Lan, LIU Bin, WANG Yan, et al. Target detection method based on convolutional neural network for SAR image[J]. Journal of Electronics Information Technology, 2016, 38(12): 3018-3025. doi: 10.11999/JEIT161032.

EOM K B and CHELLAPPA R. Noncooperative target classification using hierarchical modeling of high-range resolution radar signatures[J]. IEEE Transactions on Signal Processing, 1997, 45(9): 2318-2327.

王海, 蔡英凤, 贾允毅, 等. 基于卷积神经网络的场景自适应道路分割算法[J]. 电子与信息学报, 2017, 39(2): 263-269. doi: 10.11999/JEIT160329.

WANG Hai, CAI Yingfeng, JIA Yunyi, et al. Scene adaptive road segmentation algorithm based on deep convolutional neural network[J]. Journal of Electronics Information Technology, 2017, 39(2): 263-269. doi: 10.11999/JEIT160329.

LANCKRIET GERT R G and GHAOUI El. Laurent. A robust minimax approach to classification[J]. Journal of Machine Learning Research, 2002, 3(13): 555-582.

刘江洪, 王宝帅, 杨元安. 基于1 bit量化的视频噪声源可行性研究[J]. 电子信息对抗技术, 2016, 31(4): 83-88.

LIU Jianghong, WANG Baoshuai, and YANG Yuanan. Study on the feasibility of 1 bit video noise generator[J]. Electronic Information Warfare Technology, 2016, 31(4): 83-88.

DU Lan, WANG Baoshuai, and LI Yanbing. Robust classification scheme for airplane targets with low resolution radar based on EMD-CLEAN feature extraction method[J]. IEEE Sensors Journal, 2013, 13(12): 4648-4662. doi: 10.1109 /JSEN.2013.2272119.

王宝帅, 杜兰, 刘宏伟. 基于经验模态分解的空中飞机目标分类[J]. 电子与信息学报, 2012, 34(9): 2116-2121. doi: 10.3724/ SP.J.1146.2012.00147.

WANG Baoshuai, DU Lan, and LIU Hongwei. Aircraft classification based on empirical mode decomposition[J]. Journal of Electronics Information Technology, 2012, 34(9): 2116-2121. doi: 10.3724/SP.J.1146.2012.00147.

潘泓, 朱亚平, 夏思宇, 等. 基于上下文信息和核熵成分分析的目标分类算法[J]. 电子学报, 2016, 44(3): 580-586. doi: 10.3969/j.issn.0372-2112.2016.03.013.

PAN Hong, ZHU Yaping, XIA Siyu, et al. Object classification using context cue and kernel entropy component analysis[J]. Acta Electronica Sinica, 2016, 44(3): 580-586. doi: 10.3969/j.issn.0372-2112.2016.03.013.

文伟, 曹雪菲, 张学峰, 等. 一种基于多极化散射机理的极化SAR图像舰船目标检测方法[J]. 电子与信息学报, 2017, 39(1): 103-109. doi: 10.11999/JEIT160204.

WEN Wei, CAO Xuefei, ZHANG Xuefeng, et al. PolSAR ship detection method based on multiple polarimetric scattering mechanisms[J]. Journal of Electronics Information Technology, 2017, 39(1): 103-109. doi: 10.11999/JEIT160204.

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