Compressive sampling matching pursuit （CoSaMP） algorithm integrates the idea of combining algorithm to ensure running speed and provides rigorous error bounds which provide a good theoretical guarantee to convergenc...Compressive sampling matching pursuit （CoSaMP） algorithm integrates the idea of combining algorithm to ensure running speed and provides rigorous error bounds which provide a good theoretical guarantee to convergence. And compressive sensing （CS） can help us ease the pressure of hardware facility from the requirements of the huge amount in information processing. Therefore, a new video coding framework was proposed, which was based on CS and curvelet transform in this paper. Firstly, this new framework uses curvelet transform and CS to the key frame of test sequence, and then gains recovery frame via CoSaMP to achieve data compress. In the classic CoSaMP method, the halting criterion is that the number of iterations is fixed. Therefore, a new stopping rule is discussed to halting the algorithm in this paper to obtain better performance. According to a large number of experimental results, we ran see that this new framework has better performance and lower RMSE. Through the analysis of the experimental data, it is found that the selection of number of measurements and sparsity level has great influence on the new framework. So how to select the optimal parameters to gain better performance deserves worthy of further study.展开更多
Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple inpu...Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.展开更多
航空发动机叶尖间隙是监控其运行状态的有效参数,现有间隙测量方法很难满足超高转速下间隙距离的奈奎斯特采样率,因此无法有效提取精确的叶尖间隙值。本文基于压缩感知原理,针对间隙距离数据特征提出一种采用K-SVD(K-singular value dec...航空发动机叶尖间隙是监控其运行状态的有效参数,现有间隙测量方法很难满足超高转速下间隙距离的奈奎斯特采样率,因此无法有效提取精确的叶尖间隙值。本文基于压缩感知原理,针对间隙距离数据特征提出一种采用K-SVD(K-singular value decomposition)字典训练稀疏基的数据重构方法,该方法首先构建出K-SVD字典稀疏基对数据进行稀疏化表示,然后使用m序列高斯随机矩阵对数据进行压缩观测,最后基于压缩欠采样观测值使用正交匹配追踪算法对数据进行重构,进而精确提取叶尖间隙值。实验结果表明,在欠采样条件下间隙距离数据可精确恢复重构,与高采样率下的间隙数据相比,重构误差不超过0.02 mm。展开更多
Iterative hard thresholding(IHT)and compressive sampling matching pursuit(CoSaMP)are two mainstream compressed sensing algorithms using the hard thresholding operator.The guaranteed performance of the two algorithms f...Iterative hard thresholding(IHT)and compressive sampling matching pursuit(CoSaMP)are two mainstream compressed sensing algorithms using the hard thresholding operator.The guaranteed performance of the two algorithms for signal recovery was mainly analyzed in terms of the restricted isometry property(RIP)of sensing matrices.At present,the best known bound using the RIP of order 3k for guaranteed performance of IHT(with the unit stepsize)isδ3k<1/√3≈0.5774,and the bound for CoSaMP using the RIP of order 4k isδ4k<0.4782.A fundamental question in this area is whether such theoretical results can be further improved.The purpose of this paper is to affirmatively answer this question and to rigorously show that the abovementioned RIP bound for guaranteed performance of IHT can be significantly improved toδ3k<(√5−1)/2≈0.618,and the bound for CoSaMP can be improved toδ4k<0.5102.展开更多
压缩感知理论是一种利用信号稀疏性或可压缩性对信号进行采样同时压缩的新颖的信号采样理论。针对稀疏度未知信号重构问题,提出了一种稀疏度自适应正交多匹配追踪重构算法。该算法在广义正交匹配算法(generalized orthogonal multi matc...压缩感知理论是一种利用信号稀疏性或可压缩性对信号进行采样同时压缩的新颖的信号采样理论。针对稀疏度未知信号重构问题,提出了一种稀疏度自适应正交多匹配追踪重构算法。该算法在广义正交匹配算法(generalized orthogonal multi matching pursuit,GOMP)基础上结合稀疏自适应思想。根据相邻阶段信号能量差自适应调整当前步长大小选取支撑集的原子个数,先大步接近,后小步逼近信号真实稀疏度,从而实现对信号精确重构。实验仿真结果表明,该算法能有效精确重构信号。具有良好的重构性能和较高的重构效率。展开更多
基金the Youth Foundation of Jiangxi Provincial Education Department,China
文摘Compressive sampling matching pursuit （CoSaMP） algorithm integrates the idea of combining algorithm to ensure running speed and provides rigorous error bounds which provide a good theoretical guarantee to convergence. And compressive sensing （CS） can help us ease the pressure of hardware facility from the requirements of the huge amount in information processing. Therefore, a new video coding framework was proposed, which was based on CS and curvelet transform in this paper. Firstly, this new framework uses curvelet transform and CS to the key frame of test sequence, and then gains recovery frame via CoSaMP to achieve data compress. In the classic CoSaMP method, the halting criterion is that the number of iterations is fixed. Therefore, a new stopping rule is discussed to halting the algorithm in this paper to obtain better performance. According to a large number of experimental results, we ran see that this new framework has better performance and lower RMSE. Through the analysis of the experimental data, it is found that the selection of number of measurements and sparsity level has great influence on the new framework. So how to select the optimal parameters to gain better performance deserves worthy of further study.
文摘Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.
文摘航空发动机叶尖间隙是监控其运行状态的有效参数,现有间隙测量方法很难满足超高转速下间隙距离的奈奎斯特采样率,因此无法有效提取精确的叶尖间隙值。本文基于压缩感知原理,针对间隙距离数据特征提出一种采用K-SVD(K-singular value decomposition)字典训练稀疏基的数据重构方法,该方法首先构建出K-SVD字典稀疏基对数据进行稀疏化表示,然后使用m序列高斯随机矩阵对数据进行压缩观测,最后基于压缩欠采样观测值使用正交匹配追踪算法对数据进行重构,进而精确提取叶尖间隙值。实验结果表明,在欠采样条件下间隙距离数据可精确恢复重构,与高采样率下的间隙数据相比,重构误差不超过0.02 mm。
基金supported by National Natural Science Foundation of China(Grant Nos.12071307 and 61571384).
文摘Iterative hard thresholding(IHT)and compressive sampling matching pursuit(CoSaMP)are two mainstream compressed sensing algorithms using the hard thresholding operator.The guaranteed performance of the two algorithms for signal recovery was mainly analyzed in terms of the restricted isometry property(RIP)of sensing matrices.At present,the best known bound using the RIP of order 3k for guaranteed performance of IHT(with the unit stepsize)isδ3k<1/√3≈0.5774,and the bound for CoSaMP using the RIP of order 4k isδ4k<0.4782.A fundamental question in this area is whether such theoretical results can be further improved.The purpose of this paper is to affirmatively answer this question and to rigorously show that the abovementioned RIP bound for guaranteed performance of IHT can be significantly improved toδ3k<(√5−1)/2≈0.618,and the bound for CoSaMP can be improved toδ4k<0.5102.
文摘压缩感知理论是一种利用信号稀疏性或可压缩性对信号进行采样同时压缩的新颖的信号采样理论。针对稀疏度未知信号重构问题,提出了一种稀疏度自适应正交多匹配追踪重构算法。该算法在广义正交匹配算法(generalized orthogonal multi matching pursuit,GOMP)基础上结合稀疏自适应思想。根据相邻阶段信号能量差自适应调整当前步长大小选取支撑集的原子个数,先大步接近,后小步逼近信号真实稀疏度,从而实现对信号精确重构。实验仿真结果表明,该算法能有效精确重构信号。具有良好的重构性能和较高的重构效率。
