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Volume 37 Issue 10
Sep.  2015
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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

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

doi: 10.11999/JEIT150272
  • Received Date: 2015-03-04
  • Rev Recd Date: 2015-06-08
  • Publish Date: 2015-10-19
  • Phase diagram is an important method to evaluate sparse microwave imaging radar performance. Phase transition boundary can characterize the trend of accurate recovery rate visibly and clearly in terms of varied SNR, sparsity and sampling ratio. In sparse microwave imaging change detection, the small variation can be accurately extracted from multiple observations using distributed compressed sensing theory for the sparse scene. Phase diagram is introduced to evaluate the performance of change detection in different sampling ratio under the conditions that the sparsity and SNR has little change. Phase diagram can be used to describe the trend of phase transition boundary and to determine the data collection bounds. Furthermore, a series of simulations and experiments are conducted to verify the practicability of phase diagram. It is available to reduce the measurements and complexity of the sparse microwave imaging system.
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  • 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, 王彦平, 谭维贤, 等. 地基SAR形变监测误差分析与实验[J]. 电子与信息学报, 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.
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