二维非重构压缩感知自适应目标检测算法
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摘要
针对重构算法影响压缩成像目标检测效率和结果的问题,提出一种二维非重构自适应阈值的红外弱小目标检测算法。基于Hadamard矩阵构建的二维观测模型,利用Hadamard矩阵的特性对压缩差分图像进行解压缩,直接解码目标的空域特征,并利用改进的自适应阈值法对解码后的图像进行目标检测,避免了重构带来的存储空间和运算时间的浪费。仿真实验表明:在单目标和多目标的情况下,该算法都可以有效检测目标,并在检测率、虚警率和运算时间等指标上具有优越性能,为压缩感知红外弱小目标检测的工程应用提供新的思路和有效算法。
A two-dimensional non-reconstruction adaptive threshold algorithm aiming at infrared small target detection was proposed, for the purpose of decreasing the reconstruction algorithms ′ negative influence on target detection′ s efficiency and results. Aiming at the two-dimentional measurement model which constructed by Hadamard matrix, the compressive subtract image was analyzed by means of Hadamard′ s property in order to decode target′ s characteristics in space domain directly. Then the decoded image was detected by utilizing the advanced adaptive threshold method, which avoided the waste of memory space and operation time caused by traditional reconstruction algorithms. Simulation experiment demonstrates that the proposed model can detect the targets on the condition of both single and multiple targets, and has superiorities on detection rate, false alarm rate and operation time than the traditional detection algorithm after reconstruction. It provides a new idea and efficient algorithm for the application of compressive sensing infrared small target detection in engineering.
A two-dimensional non-reconstruction adaptive threshold algorithm aiming at infrared small target detection was proposed, for the purpose of decreasing the reconstruction algorithms ′ negative influence on target detection′ s efficiency and results. Aiming at the two-dimentional measurement model which constructed by Hadamard matrix, the compressive subtract image was analyzed by means of Hadamard′ s property in order to decode target′ s characteristics in space domain directly. Then the decoded image was detected by utilizing the advanced adaptive threshold method, which avoided the waste of memory space and operation time caused by traditional reconstruction algorithms. Simulation experiment demonstrates that the proposed model can detect the targets on the condition of both single and multiple targets, and has superiorities on detection rate, false alarm rate and operation time than the traditional detection algorithm after reconstruction. It provides a new idea and efficient algorithm for the application of compressive sensing infrared small target detection in engineering.
引文
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[3] Haupt J, Castro R, Nowak R, et al. Compressive sampling for signal classification[C]//Asilomar Conference on Signals,Systems, and Computers, 2006:1430-1434.
[4] Wei Daozhi, Huang Shucai, Zhao Yan, et al. Non-negative sparse representation for anomaly detection in hyperspectral imagery[J]. Infrared and Laser Engineering, 2016, 45(S2):S223001.(in Chinese)韦道知,黄树彩,赵岩.非负谱稀疏表示的高光谱成像中的异常检测[J].红外与激光工程, 2016, 45(S2):S223001.
[5] Li Shen, Ma Caiwen, Li Yan. Survey on reconstruction algorithm based on compressive sensing[J]. Infrared and Laser Engineering, 2013, 42(S1):225-232.(in Chinese)李珅,马彩文,李艳,等.压缩感知重构算法综述[J].红外与激光工程, 2013, 42(S1):225-232.
[6] Wang Zhongliang, Feng Wentian, Nian Yongjian.Compressive-sensing-based lossy compression for hyperspectral images using spectral unmixing[J]. Infrared and Laser Engineering, 2018, 47(S1):S126003.(in Chinese)
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[9] Ghaffari A, Babaie-Zadeh M, Jutten C. Sparse decomposition of two dimensional signals′[C]//IEEE Int Conf Acoustics,Speech Signal Processing, 2009:3157-3160.
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[11] Cui Ping, Ni Lin. Joint reconstruction algorithm for distributed compressed sensing[J]. Infrared and Laser Engineering, 2015, 44(12):3825-3830
[12] Hao Yunsheng, Ye Yishan, Deng Zhenmiao, et al. FEKO sparse micro-doppler modeling and CS reconstruction method[J]. Optics and Precision Engineering, 2016, 24(6):1482-1489.(in Chinese)郝云胜,叶艺山,邓振淼,等. FEKO稀疏微多普勒建模及CS重构方法[J].光学精密工程, 2016, 24(6):1482-1489.
[13] Ma Yongkui, Xu Peng, Gao Yulong. Optimization of the measurement matrix used for CS-based non-reconstruction detection method in cognitive radio[C]//International Conference on Instrumentation&Measurement, 2014:484-488.
[14] Xu Wenbo, Yan Zhihua, Tian Yun, et al. Detection with compressive measurements corrupted by sparse errors[C]//International Conference on Wireles Communications and Signal Processing, 2017:1-5.
[15] Li Shaoyi, Liang Shuang, Zhang Kai, et al. Research of infrared compressive imaging based point target tracking method[J]. Journal of Electronics&Information Technology, 2015, 37(7):1639-1645.(in Chinese)李少毅,梁爽,张凯,等.基于红外压缩成像的点目标跟踪方法研究[J].电子与信息学报, 2015, 37(7):1639-1645.
[16] Tian Wenbiao, Rui Guosheng, Zhang Haibo, et al. A 2-dimensional measurement model-oriented compressed Sensing reconfiguration algorithm[J]. Journal of Astronautics, 2014,35(9):1072-1077.(in Chinese)田文飚,芮国胜,张海波,等.一种面向二维观测模型的压缩感知重构算法[J].宇航学报, 2014, 35(9):1072-1077.
[17] Shan Yugang, Wang Jiabao, Hao Feng. Research on surendra background extraction method based on adaptive threshold[J].Computer Technology and Development, 2017, 27(7):91-95.(in Chinese)单玉刚,汪家宝,郝峰.基于自适应阈值的Surendra背景提取方法研究[J].计算机技术与发, 2017, 27(7):91-95.
[18] Wang Xin, Ji Tongbo, Liu Fu. Fusion of infrared and visible images based on target segmentation and compressed sensing[J]. Optics and Precision Engineering, 2016, 24(7):1743-1753.(in Chinese)王昕,吉桐伯,刘富.结合目标提取和压缩感知的红外与可见光图像融合[J].光学精密工程, 2016, 24(7):1743-1753.
[2] Haupt J, Nowak R. Compressive sampling for signal detection[C]//IEEE International Conference on Acoustics, 2007:1509-1512.
[3] Haupt J, Castro R, Nowak R, et al. Compressive sampling for signal classification[C]//Asilomar Conference on Signals,Systems, and Computers, 2006:1430-1434.
[4] Wei Daozhi, Huang Shucai, Zhao Yan, et al. Non-negative sparse representation for anomaly detection in hyperspectral imagery[J]. Infrared and Laser Engineering, 2016, 45(S2):S223001.(in Chinese)韦道知,黄树彩,赵岩.非负谱稀疏表示的高光谱成像中的异常检测[J].红外与激光工程, 2016, 45(S2):S223001.
[5] Li Shen, Ma Caiwen, Li Yan. Survey on reconstruction algorithm based on compressive sensing[J]. Infrared and Laser Engineering, 2013, 42(S1):225-232.(in Chinese)李珅,马彩文,李艳,等.压缩感知重构算法综述[J].红外与激光工程, 2013, 42(S1):225-232.
[6] Wang Zhongliang, Feng Wentian, Nian Yongjian.Compressive-sensing-based lossy compression for hyperspectral images using spectral unmixing[J]. Infrared and Laser Engineering, 2018, 47(S1):S126003.(in Chinese)
[7] Jahromi M J, Kahaei M H. Two-dimensional iterative adaptive approach for sparse matrix solution[J]. Electronics Letters, 2014, 50(1):45-47.
[8] Aboozar Ghaffari, Massoud Babaie-Zadeh, Christian Jutten.Sparse decomposition of two dimensional signals[C]//ICASSP, 2009:3157-3460.
[9] Ghaffari A, Babaie-Zadeh M, Jutten C. Sparse decomposition of two dimensional signals′[C]//IEEE Int Conf Acoustics,Speech Signal Processing, 2009:3157-3160.
[10] Van Den Berg E, Friedlander M P. SPGL1:a solver for large-scale sparse reconstruction[EB/OL].[2013-04-09].http://www.cs.ubc.ca/?mpf/spgl1.
[11] Cui Ping, Ni Lin. Joint reconstruction algorithm for distributed compressed sensing[J]. Infrared and Laser Engineering, 2015, 44(12):3825-3830
[12] Hao Yunsheng, Ye Yishan, Deng Zhenmiao, et al. FEKO sparse micro-doppler modeling and CS reconstruction method[J]. Optics and Precision Engineering, 2016, 24(6):1482-1489.(in Chinese)郝云胜,叶艺山,邓振淼,等. FEKO稀疏微多普勒建模及CS重构方法[J].光学精密工程, 2016, 24(6):1482-1489.
[13] Ma Yongkui, Xu Peng, Gao Yulong. Optimization of the measurement matrix used for CS-based non-reconstruction detection method in cognitive radio[C]//International Conference on Instrumentation&Measurement, 2014:484-488.
[14] Xu Wenbo, Yan Zhihua, Tian Yun, et al. Detection with compressive measurements corrupted by sparse errors[C]//International Conference on Wireles Communications and Signal Processing, 2017:1-5.
[15] Li Shaoyi, Liang Shuang, Zhang Kai, et al. Research of infrared compressive imaging based point target tracking method[J]. Journal of Electronics&Information Technology, 2015, 37(7):1639-1645.(in Chinese)李少毅,梁爽,张凯,等.基于红外压缩成像的点目标跟踪方法研究[J].电子与信息学报, 2015, 37(7):1639-1645.
[16] Tian Wenbiao, Rui Guosheng, Zhang Haibo, et al. A 2-dimensional measurement model-oriented compressed Sensing reconfiguration algorithm[J]. Journal of Astronautics, 2014,35(9):1072-1077.(in Chinese)田文飚,芮国胜,张海波,等.一种面向二维观测模型的压缩感知重构算法[J].宇航学报, 2014, 35(9):1072-1077.
[17] Shan Yugang, Wang Jiabao, Hao Feng. Research on surendra background extraction method based on adaptive threshold[J].Computer Technology and Development, 2017, 27(7):91-95.(in Chinese)单玉刚,汪家宝,郝峰.基于自适应阈值的Surendra背景提取方法研究[J].计算机技术与发, 2017, 27(7):91-95.
[18] Wang Xin, Ji Tongbo, Liu Fu. Fusion of infrared and visible images based on target segmentation and compressed sensing[J]. Optics and Precision Engineering, 2016, 24(7):1743-1753.(in Chinese)王昕,吉桐伯,刘富.结合目标提取和压缩感知的红外与可见光图像融合[J].光学精密工程, 2016, 24(7):1743-1753.