GOMP改进算法在信道估计中的应用
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摘要
广义正交匹配追踪GOMP(Generalized Orthogonal Matching Pursuit)算法作为压缩感知理论中的重要组成部分,在信道估计领域早有应用。但由于无法解决信道稀疏度的获取问题,限制了这一算法的发展。针对这一问题,提出一种通过变步长实现稀疏自适应匹配的改进算法,并利用傅里叶变换的共轭对称性在选择原子方面加以完善,从而提高了算法的精度和效率,增强了该算法在实际信道估计中的可行性。
Generalized Orthogonal Matching Pursuit( GOMP) algorithm has been widely used in the field of channel estimation as an important part of the compressed sensing theory. However,the inability to solve the channel sparseness acquisition problem limits the development of this algorithm. In order to solve this problem,this paper proposed an improved algorithm to achieve sparse adaptive matching through variable step size,and used the conjugate symmetry of Fourier transform to improve the selected atom,which improved the accuracy and efficiency of the algorithm and enhanced the feasibility of the algorithm in the actual channel estimation.
Generalized Orthogonal Matching Pursuit( GOMP) algorithm has been widely used in the field of channel estimation as an important part of the compressed sensing theory. However,the inability to solve the channel sparseness acquisition problem limits the development of this algorithm. In order to solve this problem,this paper proposed an improved algorithm to achieve sparse adaptive matching through variable step size,and used the conjugate symmetry of Fourier transform to improve the selected atom,which improved the accuracy and efficiency of the algorithm and enhanced the feasibility of the algorithm in the actual channel estimation.
引文
[1]焦李成,杨淑媛,刘芳,等.压缩感知回顾与展望[J].电子学报,2011,39(7):1651-1662.
[2]Donoho D L,Tsaig Y.Extensions of compressed sensing[J].Signal Processing,2006,86(3):533-548.
[3]Tropp J A,Gilbert A C.Signal Recovery from Random Measurements via Orthogonal Matching Pursuit[J].IEEE Transactions on Information on Information Theory,2007,53(12):4655-4666.
[4]李然,干宗良,崔子冠,等.压缩感知图像重建算法的研究现状及其展望[J].电视技术,2013,37(19):192-196.
[5]Kim Seung-Jean,Koh K,Lustig M,et al.An Interior-Point Method for Large-Scale l1-Regularized Least Squares[J].IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617.
[6]何雪云,宋荣方,周克琴.基于压缩感知的OFDM系统系数信道估计新方法研究[J].南京邮电大学学报,2010,30(2):60-65.
[7]Wang J,Kwon S,Shim B.Generalized orthogonal matching pursuit[J].IEEE transactions on signal processing,2012(60):6202-6216.
[8]Cotter S F,Rao B D.Sparse channel estimation via matching pursuit algorithm[J].IEEE transactions on Communications,2002,50(3):374-377.
[9]Karabulut G Z,Yongacoglu A.Sparse channel estimation using orthogonal matching pursuit algorithm[J].IEEE 60th Vehicular Technology Conference,2004.VTC2004-Fall.2004.
[10]甘伟,许录平,罗楠,等.一种自适应压缩感知重构算法[J].系统工程及电子设计,2011,33(9):1948-1953.
[11]吕伟杰,陈霞,刘红珍.基于压缩感知的自适应匹配追踪优化[J].系统工程与电子技术,2015,37(5):1201-1205.
[12]Donoho D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[13]Fiqueiredo M A T,Nowak R D,Wright S J.Gradient projection for sqarse reconstruction:application to compressed sensing and other inverse problems[J].IEEE Journal of Selected Topics in Signal Processing,2007,1(4):586-597.
[14]甘伟,许录平,苏哲.一种压缩感知重构算法[J].电子与信息学报,2010,32(9):2151-2155.
[15]赵龙慧,潘乐炳,李宝清.OFDM稀疏信道估计中改进的OMP算法[J].计算机工程与设计,2015,36(7):1701-1705.
[16]Davenport M A,Boufounos P T,Wakin M B,et al.Signal Processing With Compressive Measurements[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(2):445-460.
[17]石光明,刘丹华,高大化.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081.
[18]吕方旭,张金成,王泉,等.基于傅里叶基的自适应压缩感知重构算法[J].北京航空航天大学学报,2014,40(4):544-550.
[19]朱延万,赵拥军,孙兵.一种改进的稀疏度自适应匹配追踪算法[J].信号处理,2012,28(1):80-86.
[20]王妮娜,桂冠,苏冰涛,等.基于压缩感知的MIMO-OFDM系统稀疏信道估计方法[J].电子科技大学学报,2013,42(1):59-62.
[21]赵龙慧,潘乐炳,李宝清.OFDM稀疏信道估计中改进的OMP算法[J].计算机工程与设计,2015,36(7):1701-1705.
[22]高西全,丁玉美.数字信号处理[M].西安:西安电子科技大学出版社,2008:84-87.
[23]杨盼.压缩感知中改进的匹配追踪类算法研究[D].安徽:安徽大学,2015.
[2]Donoho D L,Tsaig Y.Extensions of compressed sensing[J].Signal Processing,2006,86(3):533-548.
[3]Tropp J A,Gilbert A C.Signal Recovery from Random Measurements via Orthogonal Matching Pursuit[J].IEEE Transactions on Information on Information Theory,2007,53(12):4655-4666.
[4]李然,干宗良,崔子冠,等.压缩感知图像重建算法的研究现状及其展望[J].电视技术,2013,37(19):192-196.
[5]Kim Seung-Jean,Koh K,Lustig M,et al.An Interior-Point Method for Large-Scale l1-Regularized Least Squares[J].IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617.
[6]何雪云,宋荣方,周克琴.基于压缩感知的OFDM系统系数信道估计新方法研究[J].南京邮电大学学报,2010,30(2):60-65.
[7]Wang J,Kwon S,Shim B.Generalized orthogonal matching pursuit[J].IEEE transactions on signal processing,2012(60):6202-6216.
[8]Cotter S F,Rao B D.Sparse channel estimation via matching pursuit algorithm[J].IEEE transactions on Communications,2002,50(3):374-377.
[9]Karabulut G Z,Yongacoglu A.Sparse channel estimation using orthogonal matching pursuit algorithm[J].IEEE 60th Vehicular Technology Conference,2004.VTC2004-Fall.2004.
[10]甘伟,许录平,罗楠,等.一种自适应压缩感知重构算法[J].系统工程及电子设计,2011,33(9):1948-1953.
[11]吕伟杰,陈霞,刘红珍.基于压缩感知的自适应匹配追踪优化[J].系统工程与电子技术,2015,37(5):1201-1205.
[12]Donoho D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[13]Fiqueiredo M A T,Nowak R D,Wright S J.Gradient projection for sqarse reconstruction:application to compressed sensing and other inverse problems[J].IEEE Journal of Selected Topics in Signal Processing,2007,1(4):586-597.
[14]甘伟,许录平,苏哲.一种压缩感知重构算法[J].电子与信息学报,2010,32(9):2151-2155.
[15]赵龙慧,潘乐炳,李宝清.OFDM稀疏信道估计中改进的OMP算法[J].计算机工程与设计,2015,36(7):1701-1705.
[16]Davenport M A,Boufounos P T,Wakin M B,et al.Signal Processing With Compressive Measurements[J].IEEE Journal of Selected Topics in Signal Processing,2010,4(2):445-460.
[17]石光明,刘丹华,高大化.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081.
[18]吕方旭,张金成,王泉,等.基于傅里叶基的自适应压缩感知重构算法[J].北京航空航天大学学报,2014,40(4):544-550.
[19]朱延万,赵拥军,孙兵.一种改进的稀疏度自适应匹配追踪算法[J].信号处理,2012,28(1):80-86.
[20]王妮娜,桂冠,苏冰涛,等.基于压缩感知的MIMO-OFDM系统稀疏信道估计方法[J].电子科技大学学报,2013,42(1):59-62.
[21]赵龙慧,潘乐炳,李宝清.OFDM稀疏信道估计中改进的OMP算法[J].计算机工程与设计,2015,36(7):1701-1705.
[22]高西全,丁玉美.数字信号处理[M].西安:西安电子科技大学出版社,2008:84-87.
[23]杨盼.压缩感知中改进的匹配追踪类算法研究[D].安徽:安徽大学,2015.