基于量子免疫克隆的压缩感知数据重构算法
|
摘要
提出了一种基于量子免疫克隆的压缩感知数据重构算法(Q-CSDR)。算法先提出了一种能够提高数据重构概率的自适应分帧方法,然后利用量子克隆免疫算法的优化组合性能实现数据的精确重构。实验结果表明,Q-CSDR算法能够根据原始信号稀疏度自动调节压缩比率,具有重构速度快,重构精度高,能够适应于高稀疏度数据重构等优点。该算法已应用于秦始皇帝陵博物院野外文物安防系统。经实际检验,收到了良好效果。
An algorithm of compressed sensor data reconstruction,called Q-CSDR,based on the algorithm of quantum-inspired immune clon,is proposed in this paper. Q-CSDR can increase the probability of data reconstruction through framing the data adaptively. Because of its excellent performance,Q-CSDR uses the algorithm to accurately reconstruct the data. The experiment results show that,according to the sparsity of the original data,the algorithm can automatically adjust compression ratio,raise the accuracy of data reconstruction and adapt well to high sparsity data reconstruction. It is used in the field security system of Emperor Qinshihuang`s mausoleum site museum with good performance.
An algorithm of compressed sensor data reconstruction,called Q-CSDR,based on the algorithm of quantum-inspired immune clon,is proposed in this paper. Q-CSDR can increase the probability of data reconstruction through framing the data adaptively. Because of its excellent performance,Q-CSDR uses the algorithm to accurately reconstruct the data. The experiment results show that,according to the sparsity of the original data,the algorithm can automatically adjust compression ratio,raise the accuracy of data reconstruction and adapt well to high sparsity data reconstruction. It is used in the field security system of Emperor Qinshihuang`s mausoleum site museum with good performance.
引文
[1]Cands E,Romberg J,Tao T.Satable singal recovery form incomplete and inaccurate measurements[J].Communications on Pure and Applied Mathematics,2006,59(8):1207-1223.
[2]杨海蓉,张成,等.压缩传感理论与重构算法[J].电子学报,2011,39(1):142-148.
[3]Chen B S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20(1):33-61.
[4]Cands E J,Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.
[5]Tropp J A,Gilbert A C.Singal recovery from random measurements via orthogonal matching pursuit[J].IEEE transactions on Information Theory,2007,52(12):4655-4666.
[6]Dai W,Milenkovic O.Subspace pursuit for compressive sensing signal reconstruction[J].IEEE Transactions on Information Theory,2009,55(5):2230-2249.
[7]Needell D,Tropp J A.CoSaMP:Iterative signal recovery form incomplete and inaccurate samples[J].Applied and Computational Harmonic Analysis,2008,26(3):301-321.
[8]Blumensath T,Davies M E.Iterative hard thresholding for compressed sensing[J].Applied and Computational Harmonic Analysis,2009,27(3):265-274.
[9]李佳,王强,沈毅,李波,等.压缩感知中测量矩阵与重建算法的协同构造[J].电子学报,2013,41(1):29-34.
[10]王娟.量子免疫克隆算法研究及在压缩感知重构中的应用[D].南京:南京邮电大学,2012.
[11]朱丰,张群,柏又青,冯有前,张维强,等.一种新的基于遗传算法的压缩感知重构方法及其在SAR高分辨距离像重构中的应用[J].控制与决策,2011,27(11):1669-1675.
[12]Thong T Do,Gan Lu,Nguyen,Tran D.Sparsity adaptive matching pursuit algorithm for practical compressed sensing.Asilomar Conference on Signals,Systems and Computers[J].Pacific Grove,California,2008(10):581-587.
[13]刘亚新,赵瑞珍,胡绍海,姜春晖.用于压缩感知信号重建的正则化自适应匹配追踪算法[J].电子与信息学报,2010,32(11):2713-2717.
[14]Licheng Jiao,Yangyang Li.Quantum-inspired Immune Clonal Optimization[C].Neural Networks and Brain,2005.ICNN&B‘05.International Conference on.2005:461-466.
[2]杨海蓉,张成,等.压缩传感理论与重构算法[J].电子学报,2011,39(1):142-148.
[3]Chen B S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing,1998,20(1):33-61.
[4]Cands E J,Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.
[5]Tropp J A,Gilbert A C.Singal recovery from random measurements via orthogonal matching pursuit[J].IEEE transactions on Information Theory,2007,52(12):4655-4666.
[6]Dai W,Milenkovic O.Subspace pursuit for compressive sensing signal reconstruction[J].IEEE Transactions on Information Theory,2009,55(5):2230-2249.
[7]Needell D,Tropp J A.CoSaMP:Iterative signal recovery form incomplete and inaccurate samples[J].Applied and Computational Harmonic Analysis,2008,26(3):301-321.
[8]Blumensath T,Davies M E.Iterative hard thresholding for compressed sensing[J].Applied and Computational Harmonic Analysis,2009,27(3):265-274.
[9]李佳,王强,沈毅,李波,等.压缩感知中测量矩阵与重建算法的协同构造[J].电子学报,2013,41(1):29-34.
[10]王娟.量子免疫克隆算法研究及在压缩感知重构中的应用[D].南京:南京邮电大学,2012.
[11]朱丰,张群,柏又青,冯有前,张维强,等.一种新的基于遗传算法的压缩感知重构方法及其在SAR高分辨距离像重构中的应用[J].控制与决策,2011,27(11):1669-1675.
[12]Thong T Do,Gan Lu,Nguyen,Tran D.Sparsity adaptive matching pursuit algorithm for practical compressed sensing.Asilomar Conference on Signals,Systems and Computers[J].Pacific Grove,California,2008(10):581-587.
[13]刘亚新,赵瑞珍,胡绍海,姜春晖.用于压缩感知信号重建的正则化自适应匹配追踪算法[J].电子与信息学报,2010,32(11):2713-2717.
[14]Licheng Jiao,Yangyang Li.Quantum-inspired Immune Clonal Optimization[C].Neural Networks and Brain,2005.ICNN&B‘05.International Conference on.2005:461-466.