The performance guarantees of generalized orthogonal matching pursuit（ gOMP） are considered in the framework of mutual coherence. The gOMP algorithmis an extension of the well-known OMP greed algorithmfor compressed...The performance guarantees of generalized orthogonal matching pursuit（ gOMP） are considered in the framework of mutual coherence. The gOMP algorithmis an extension of the well-known OMP greed algorithmfor compressed sensing. It identifies multiple N indices per iteration to reconstruct sparse signals.The gOMP with N≥2 can perfectly reconstruct any K-sparse signals frommeasurement y = Φx if K 〈1/N（1/μ-1） ＋1,where μ is coherence parameter of measurement matrix Φ. Furthermore,the performance of the gOMP in the case of y = Φx ＋ e with bounded noise ‖e‖2≤ε is analyzed and the sufficient condition ensuring identification of correct indices of sparse signals via the gOMP is derived,i. e.,K 〈1/N（1/μ-1）＋1-（2ε/Nμxmin） ,where x min denotes the minimummagnitude of the nonzero elements of x. Similarly,the sufficient condition in the case of G aussian noise is also given.展开更多
现有多跳频信号参数估计方法稀疏线性回归(Sparse Linear Regression,SLR)存在计算量大、内存消耗大的缺点。事实上,频率跳变只在少数几个数据点上发生,大部分数据不包含跳变信息。基于此,提出一种基于正交匹配追踪(Orthogonal Matching...现有多跳频信号参数估计方法稀疏线性回归(Sparse Linear Regression,SLR)存在计算量大、内存消耗大的缺点。事实上,频率跳变只在少数几个数据点上发生,大部分数据不包含跳变信息。基于此,提出一种基于正交匹配追踪(Orthogonal Matching Pursuit,OMP)和SLR相结合的跳频信号参数估计方法。该方法将接收到的样本数据均匀分段,对每段数据用OMP算法预处理,检测出发生频率跳变的数据段以及估计出没有发生跳变的数据段的频率;对这些发生跳变的数据段分别用SLR算法估计得到各段的跳时和频率;拼接可以得到整个样本的跳时、跳频图案等。仿真结果表明,该方法在在保持SLR精确估计性能的同时,能有效减少计算量。展开更多
As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algor...As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.展开更多
基金Supported by the National Natural Science Foundation of China(60119944,61331021)the National Key Basic Research Program Founded by MOST(2010C B731902)+1 种基金the Program for Changjiang Scholars and Innovative Research Team in University(IRT1005)Beijing Higher Education Young Elite Teacher Project(YET P1159)
文摘The performance guarantees of generalized orthogonal matching pursuit（ gOMP） are considered in the framework of mutual coherence. The gOMP algorithmis an extension of the well-known OMP greed algorithmfor compressed sensing. It identifies multiple N indices per iteration to reconstruct sparse signals.The gOMP with N≥2 can perfectly reconstruct any K-sparse signals frommeasurement y = Φx if K 〈1/N（1/μ-1） ＋1,where μ is coherence parameter of measurement matrix Φ. Furthermore,the performance of the gOMP in the case of y = Φx ＋ e with bounded noise ‖e‖2≤ε is analyzed and the sufficient condition ensuring identification of correct indices of sparse signals via the gOMP is derived,i. e.,K 〈1/N（1/μ-1）＋1-（2ε/Nμxmin） ,where x min denotes the minimummagnitude of the nonzero elements of x. Similarly,the sufficient condition in the case of G aussian noise is also given.
文摘现有多跳频信号参数估计方法稀疏线性回归(Sparse Linear Regression,SLR)存在计算量大、内存消耗大的缺点。事实上,频率跳变只在少数几个数据点上发生,大部分数据不包含跳变信息。基于此,提出一种基于正交匹配追踪(Orthogonal Matching Pursuit,OMP)和SLR相结合的跳频信号参数估计方法。该方法将接收到的样本数据均匀分段,对每段数据用OMP算法预处理,检测出发生频率跳变的数据段以及估计出没有发生跳变的数据段的频率;对这些发生跳变的数据段分别用SLR算法估计得到各段的跳时和频率;拼接可以得到整个样本的跳时、跳频图案等。仿真结果表明,该方法在在保持SLR精确估计性能的同时,能有效减少计算量。
基金supported by the Fundamental Research Funds for the Central Universities(DL13BB21)the Natural Science Foundation of Heilongjiang Province(C2015054)+1 种基金Heilongjiang Province Technology Foundation for Selected Osverseas ChineseNatural Science Foundation of Heilongjiang Province(F2015036)
文摘As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.
