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.
王宝帅,刘江洪,郑小亮,贺岷珏,肖庆. 基于特征谱特征的机场跑道异物分层检测算法[J]. 电子与信息学报, 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. JEIT, 2017, 39(11): 2690-2696.
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.
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.
[4]
ANAS Tom and VISWANATHAN R. Switched order statistics CFAR test for target detection[C]. 2008 IEEE Radar Conference, Rome, Italy, 2008: 1-5.
[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.
[6]
NITZBERG R. Clutter map CFAR analysis[J]. IEEE Transactions on Aerospace and Electronic Systems, 1986, 22(4): 419-421.
[7]
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.
[8]
SCHLEHER D C. Radar detection in Weibull clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 1976, 12(6): 736-743.
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.
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.
[11]
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.
[12]
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.
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.
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.
[15]
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.
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.
[17]
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.
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.
[19]
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.
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.
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.
