基于深度生成對(duì)抗網(wǎng)絡(luò)的海雜波數(shù)據(jù)增強(qiáng)方法
doi: 10.11999/JEIT200447
-
1.
西安文理學(xué)院 西安 710065
-
2.
西安石油大學(xué) 西安 710065
-
3.
西安電子科技大學(xué) 西安 710071
Sea Clutter Data Augmentation Method Based on Deep Generative Adversarial Network
-
1.
Xi’an University, Xi’an 710065, China
-
2.
Xi’an Shiyou University, Xi’an 710065, China
-
3.
Xidian University, Xi’an 710071, China
-
摘要: 海雜波數(shù)據(jù)稀缺,獲取海雜波數(shù)據(jù)成本高、周期長(zhǎng),極大地限制了海雜波特性研究及海洋遙感應(yīng)用。該文主要研究了基于深度生成性對(duì)抗網(wǎng)絡(luò)(GAN)的海雜波數(shù)據(jù)生成方法,通過(guò)擴(kuò)展傳統(tǒng)的GAN框架,形成了1維海雜波數(shù)據(jù)生成和鑒別模型,基于實(shí)測(cè)海雜波數(shù)據(jù)集,進(jìn)行對(duì)抗網(wǎng)絡(luò)生成和鑒別模型訓(xùn)練,分析了生成模型所生成的海雜波數(shù)據(jù)的幅度分布特性和時(shí)間、空間相關(guān)性?;趯?shí)測(cè)數(shù)據(jù)驗(yàn)證了該方法能夠生成更多、更多樣、與真實(shí)海雜波數(shù)據(jù)分布相近的海雜波數(shù)據(jù)。
-
關(guān)鍵詞:
- 生成性對(duì)抗網(wǎng)絡(luò) /
- 海雜波 /
- 幅度分布特性 /
- 時(shí)間相關(guān)性
Abstract: Due to the scarcity of sea clutter data, the high cost and long period of obtaining sea clutter data greatly limit the research of sea clutter characteristics and the application of ocean remote sensing. The method of sea clutter data generation based on the Generative Adversarial Networks (GAN) is studied. By extending the traditional GAN framework, a one-dimensional sea clutter data generation and identification model is formed. Based on the radar measured sea clutter data set, the generation and identification model training in the adversarial network is carried out. The amplitude distribution characteristics and time and spatial correlation of the sea clutter data generated by the model are analyzed. Based on the measured data, it is verified that the method can generate more sea clutter data with more variety, and similar distribution to the real sea clutter data. -
表 1 生成器、辨別器網(wǎng)絡(luò)參數(shù)
生成器網(wǎng)絡(luò) 判別器網(wǎng)絡(luò) Layer Act./Norm Output shape Layer Act./Norm Output shape Fully Linear ReLU Conv1d Leaky ReLU 64×2048 BatchNorm1d 1×256 Conv1d Leaky ReLU 128×512 Conv1d ReLU 512×512 Conv1d Leaky ReLU 256×128 Conv1d ReLU 256×1024 Conv1d Leaky ReLU 512×128 Conv1d ReLU 128×1024 Conv1d Leaky ReLU 1024×16 Conv1d ReLU 64×4096 Fully Linear sigmoid 1×1 Conv1d Tanh 1×8192 下載: 導(dǎo)出CSV
-
[1] 劉寧波, 董云龍, 王國(guó)慶, 等. X波段雷達(dá)對(duì)海探測(cè)試驗(yàn)與數(shù)據(jù)獲取[J]. 雷達(dá)學(xué)報(bào), 2019, 8(5): 656–667. doi: 10.12000/JR19089LIU Ningbo, DONG Yunlong, WANG Guoqing, et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8(5): 656–667. doi: 10.12000/JR19089 [2] DING Hao, GUAN Jian, LIU Ningbo, et al. Modeling of heavy tailed sea clutter based on the generalized central limit theory[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(11): 1591–1595. doi: 10.1109/LGRS.2016.2596322 [3] TITI G W and MARSHALL D F. The ARPA/NAVY mountaintop program: Adaptive signal processing for airborne early warning radar[C]. 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, Atlanta, USA, 1996: 1165–1168. [4] DROSOPOULOS A. Description of the OHGR database[R]. Technical Note 94–14, 1994. [5] GRECO M, STINCO P, GINI F, et al. Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 1502–1513. doi: 10.1109/TAES.2010.5545205 [6] 王帥. 基于人工智能(GAN)的影像技術(shù)探究[D]. [碩士論文], 南京師范大學(xué), 2019. [7] 雷志勇, 黃忠平, 吳剛, 等. 機(jī)載L波段雷達(dá)海雜波幅度分布特性分析[J]. 電波科學(xué)學(xué)報(bào), 2019, 34(5): 558–566.LEI Zhiyong, HUANG Zhongping, WU Gang, et al. Analysis of sea clutter distribution with L-band airborne radar[J]. Chinese Journal of Radio Science, 2019, 34(5): 558–566. [8] 劉恒燕, 宋杰, 熊偉, 等. 大入射余角海雜波相關(guān)特性分析及幅度擬合[J]. 海軍航空工程學(xué)院學(xué)報(bào), 2018, 33(3): 307–312. doi: 10.7682/j.issn.1673-1522.2018.03.009LIU Hengyan, SONG Jie, XIONG Wei, et al. Sea clutter correlation analysis and amplitude fitting for large grazing angle[J]. Journal of Naval Aeronautical and Astronautical University, 2018, 33(3): 307–312. doi: 10.7682/j.issn.1673-1522.2018.03.009 [9] 傅俊滔, 周國(guó)安, 陳紅. 基于ZMNL的Pareto雜波模擬改進(jìn)方法[J]. 彈箭與制導(dǎo)學(xué)報(bào), 2019, 39(4): 19–21, 28.FU Juntao, ZHOU Guoan, and CHEN Hong. Improved method of Pareto clutter simulation based on ZMNL[J]. Journal of Projectiles,Rockets,Missiles and Guidance, 2019, 39(4): 19–21, 28. [10] 王坤峰, 左旺孟, 譚營(yíng), 等. 生成式對(duì)抗網(wǎng)絡(luò): 從生成數(shù)據(jù)到創(chuàng)造智能[J]. 自動(dòng)化學(xué)報(bào), 2018, 44(5): 769–774.WANG Kunfeng, ZUO Wangmeng, TAN Ying, et al. Generative adversarial networks: from generating data to creating intelligence[J]. Acta Automatica Sinica, 2018, 44(5): 769–774. [11] 徐雅楠, 劉寧波, 丁昊, 等. 利用CNN的海上目標(biāo)探測(cè)背景分類方法[J]. 電子學(xué)報(bào), 2019, 47(12): 2505–2514.XU Yanan, LIU Ningbo, DING Hao, et al. Background classification method for marine target detection based on CNN[J]. Acta Electronica Sinica, 2019, 47(12): 2505–2514. [12] 丁昊, 劉寧波, 董云龍, 等. 雷達(dá)海雜波測(cè)量試驗(yàn)回顧與展望[J]. 雷達(dá)學(xué)報(bào), 2019, 8(3): 281–302. doi: 10.12000/JR19006DING Hao, LIU Ningbo, DONG Yunlong, et al. Overview and prospects of radar sea clutter measurement experiments[J]. Journal of Radars, 2019, 8(3): 281–302. doi: 10.12000/JR19006 [13] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[C]. Conference on Neural Information Processing Systems (NIPS), Montreal Canada, 2014: 1–23. [14] ZHANG Zhimian, WANG Haipeng, XU Feng, et al. Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 7177–7188. [15] ARJOVSKY M, CHINTALA S, and BOTTOU L. Wasserstein GAN[C]. International Conference on Machine Learning, Sydneym, Australia, 2017: 1–32. [16] GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[C]. The 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 5767–5777. [17] 關(guān)鍵, 丁昊, 黃勇, 等. 實(shí)測(cè)海雜波數(shù)據(jù)空間相關(guān)性研究[J]. 電波科學(xué)學(xué)報(bào), 2012, 27(5): 943–953.GUAN Jian, DING Hao, HUANG Yong, et al. Spatial correlation property with measured sea clutter data[J]. Chinese Journal of Radio Science, 2012, 27(5): 943–953. -