一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

姓名
郵箱
手機號碼
標題
留言內容
驗證碼

相變圖在稀疏微波成像變化檢測降采樣分析中的應用

田野 畢輝 張冰塵 洪文

田野, 畢輝, 張冰塵, 洪文. 相變圖在稀疏微波成像變化檢測降采樣分析中的應用[J]. 電子與信息學報, 2015, 37(10): 2335-2341. doi: 10.11999/JEIT150272
引用本文: 田野, 畢輝, 張冰塵, 洪文. 相變圖在稀疏微波成像變化檢測降采樣分析中的應用[J]. 電子與信息學報, 2015, 37(10): 2335-2341. doi: 10.11999/JEIT150272
Application of Phase Diagram to Sampling Ratio Analysis in Sparse Microwave Imaging Change Detection[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2335-2341. doi: 10.11999/JEIT150272
Citation: Application of Phase Diagram to Sampling Ratio Analysis in Sparse Microwave Imaging Change Detection[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2335-2341. doi: 10.11999/JEIT150272

相變圖在稀疏微波成像變化檢測降采樣分析中的應用

doi: 10.11999/JEIT150272

Application of Phase Diagram to Sampling Ratio Analysis in Sparse Microwave Imaging Change Detection

  • 摘要: 相變圖是稀疏微波成像雷達性能評估的一種重要方式,它可以準確刻畫出雷達成像性能隨稀疏度、采樣比和信噪比3個參數(shù)的變化趨勢,給出不同參數(shù)組合下場景準確重建的概率值。稀疏微波成像變化檢測中,由于場景的變化相對于整個觀測區(qū)域是稀疏的,利用分布式壓縮感知方法可以在采樣比組合滿足一定條件下準確提取場景變化量。該文在場景稀疏度和信噪比不變的情況下,研究前后觀測數(shù)據的采樣比對變化檢測結果的影響,繪制稀疏微波成像變化檢測相變圖,并利用相變圖分析變化檢測結果隨前后兩次觀測的采樣比參數(shù)的變化趨勢,確定可以實現(xiàn)準確重建的采樣比參數(shù)組合范圍。最后通過仿真和實驗驗證相變圖用于分析稀疏微波成像變化檢測結果的可行性和有效性,為實際稀疏微波成像系統(tǒng)降低數(shù)據采集量和系統(tǒng)設計復雜度提供依據。
  • Bruzzone L and Prieto D. Automatic analysis of the difference image for unsupervised change detection[J]. IEEE Transactions on Geoscience Remote Sensing, 2000, 38(3): 1171-1182.
    Cha M, Nam M, and Geyer K. Joint SAR image compression and coherent change detection[C]. 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, 2014: 13-16.
    曲世勃, 王彥平, 譚維賢, 等. 地基SAR形變監(jiān)測誤差分析與實驗[J]. 電子與信息學報, 2011, 33(1): 1-7.
    Qu Shi-bo, Wang Yan-ping, Tan Wei-xian, et al.. Deformation detection error analysis and experiment using ground based SAR[J]. Journal of Electronics Information Technology, 2011, 33(1): 1-7.
    Zhang Bing-chen, Hong Wen, and Wu Yi-rong. Sparse microwave imaging: principles and applications[J]. Science China Information Sciences, 2012, 55(8): 1722-1754.
    Patel V M, Easley G R, Healy D M, et al.. Compressed synthetic aperture radar[J]. IEEE Transactions on Signal Processing, 2010, 4(2): 244-254.
    Hong Wen, Zhang Bing-chen, Zhang Zhe, et al.. Radar imaging with sparse constraint: principle and initial experiment[C]. 10th European Conference on Synthetic Aperture Radar (EUSAR), Berlin, 2014: 1-4.
    Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
    Stojanovic I, Novak L, and Karl W C. Interrupted SAR persistent surveillance via group sparse reconstruction of multipass data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 987-1003.
    Hou Biao, Wei Qian, Zheng Yao-guo, et al.. Unsupervised change detection in SAR image based on Gauss-Log ratio image fusion and compressed projection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3297-3317.
    Marco F D, Shriram S, Dror B D, et al.. Distributed compressed sensing of jointly sparse signals[C]. 39th Asilomar Conference on Signals, Systems and Computers, CA, USA, 2005: 1537-1541.
    Lin Yue-guan, Zhang Bing-chen, Hong Wen, et al.. Multi- channel SAR imaging based on distributed compressive sensing[J]. Science China Information Sciences, 2012, 55(2): 245-259.
    Candes E, Romberg J, and Tao T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223.
    Candes E and Tao T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203-4215.
    Liu Jing-bo, Jin Jian, and Gu Yuan-tao. Relation between exact and robust recovery for F-minimization: a topological viewpoint[C]. 2013 IEEE International Symposium on Information Theory Proceedings (ISIT), Istanbul, 2013: 859-863.
    Ben-Haim Z, Eldar Y, and Elad M. Coherence-based performance guarantees for estimating a sparse vector under random noise[J]. IEEE Transactions on Signal Processing, 2010, 58(10): 5030-5043.
    Donoho D L, Malekiy A, and Montanari A. The noise-sensitivity phase transition in compressed sensing[J]. IEEE Transactions on Information Theory, 2011, 57(10): 6920-6941.
    Donoho D L, Johnstone I, and Montanari A. Accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising[J]. IEEE Transactions on Information Theory, 2013, 59(6): 3396-3433.
    Tian Ye, Jiang Cheng-long, Lin Yue-guan, et al.. An evaluation method for sparse microwave imaging radar system using phase diagrams[C]. Radar 2011 IEEE CIE International Conference, Chengdu, 2011, 1: 210-213.
    Xiao Peng, Li Chun-sheng, and Yu Ze. Effects of noise, sampling rate and signal sparsity for compressed sensing Synthetic Aperture Radar pulse compression[C]. 2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC, 2011: 656-659.
  • 加載中
計量
  • 文章訪問數(shù):  1566
  • HTML全文瀏覽量:  90
  • PDF下載量:  480
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2015-03-04
  • 修回日期:  2015-06-08
  • 刊出日期:  2015-10-19

目錄

    /

    返回文章
    返回