基于特征譜特征的機(jī)場跑道異物分層檢測算法

王寶帥; 劉江洪; 鄭小亮; 賀岷玨; 肖慶

doi:10.11999/JEIT170178

基于特征譜特征的機(jī)場跑道異物分層檢測算法

doi: 10.11999/JEIT170178

計量
- 文章訪問數(shù): 1487
- HTML全文瀏覽量: 208
- PDF下載量: 281
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-03-01
- 修回日期: 2017-08-25
- 刊出日期: 2017-11-19

A Hierarchical FOD Detection Method Based on Eigenvalue Spectrum Features

摘要

摘要: 強雜波背景下的弱靜止目標(biāo)檢測是機(jī)場跑道異物(Foreign Object Debris, FOD)監(jiān)測雷達(dá)面臨的關(guān)鍵問題。該文提出一種基于特征譜特征和最小最大概率機(jī)(Minimax Probability Machine, MPM)的FOD分層檢測算法，首先利用雜波圖恒虛警(Constant False Alarm Rate, CFAR)將雷達(dá)錄取回波中的背景雜波和FOD回波(包含虛警)區(qū)分開，然后提取特征譜特征將在回波域中差異較小的FOD回波和虛警回波轉(zhuǎn)換到區(qū)分性更大的特征域，最后利用MPM分類器實現(xiàn)對FOD和虛警的分類，從而達(dá)到降低虛警次數(shù)的目的?；趯崪y數(shù)據(jù)的試驗結(jié)果表明，所提方法可以獲得較好的檢測性能。
- 毫米波雷達(dá) /
- 機(jī)場跑道異物 /
- 特征提取 /
- 最小最大概率機(jī)
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)

HTML全文

參考文獻(xiàn)(33)

張思睿, 葛萬成, 汪亮友. 惡劣天氣下可見光和紅外圖像融合算法設(shè)計[J]. 信息技術(shù), 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.

李華瓊, 張中僅, 王雨果, 等. CFAR方法在機(jī)場跑道FOD檢測中的性能分析[J]. 無線電工程, 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.

張群, 何其芳, 羅迎. 基于貝塞爾函數(shù)基信號分解的微動群目標(biāo)特征提取方法[J]. 電子與信息學(xué)報, 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.

杜蘭, 史蕙若, 李林森, 等. 基于分?jǐn)?shù)階傅立葉變換的窄帶雷達(dá)飛機(jī)目標(biāo)回波特征提取方法[J]. 電子與信息學(xué)報, 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.

李彥兵, 杜蘭, 劉宏偉, 等. 基于微多普勒特征的地面目標(biāo)分類[J]. 電子與信息學(xué)報, 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.

杜蘭, 劉彬, 王燕, 等. 基于卷積神經(jīng)網(wǎng)絡(luò)的SAR圖像目標(biāo)檢測算法[J]. 電子與信息學(xué)報, 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, 蔡英鳳, 賈允毅, 等. 基于卷積神經(jīng)網(wǎng)絡(luò)的場景自適應(yīng)道路分割算法[J]. 電子與信息學(xué)報, 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.

劉江洪, 王寶帥, 楊元安. 基于1 bit量化的視頻噪聲源可行性研究[J]. 電子信息對抗技術(shù), 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.

王寶帥, 杜蘭, 劉宏偉. 基于經(jīng)驗?zāi)B(tài)分解的空中飛機(jī)目標(biāo)分類[J]. 電子與信息學(xué)報, 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, 朱亞平, 夏思宇, 等. 基于上下文信息和核熵成分分析的目標(biāo)分類算法[J]. 電子學(xué)報, 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.

文偉, 曹雪菲, 張學(xué)峰, 等. 一種基于多極化散射機(jī)理的極化SAR圖像艦船目標(biāo)檢測方法[J]. 電子與信息學(xué)報, 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.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問統(tǒng)計