A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with...A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with the recovery of fully perturbed low-rank matrices. By utilizing the p-null space property (p-NSP) and the p-restricted isometry property (p-RIP) of the matrix, sufficient conditions to ensure that the stable and accurate reconstruction for low-rank matrix in the case of full perturbation are derived, and two upper bound recovery error estimation ns are given. These estimations are characterized by two vital aspects, one involving the best r-approximation error and the other concerning the overall noise. Specifically, this paper obtains two new error upper bounds based on the fact that p-RIP and p-NSP are able to recover accurately and stably low-rank matrix, and to some extent improve the conditions corresponding to RIP.展开更多
In a jamming environment with multiple wideband and narrowband jammers, global positioning system （GPS） receivers can use space-time processing to efficiently suppress the jamming. However, the computational complex...In a jamming environment with multiple wideband and narrowband jammers, global positioning system （GPS） receivers can use space-time processing to efficiently suppress the jamming. However, the computational complexity of space-time algorithms restricts their application in practical GPS receivers. This paper describes a reduced-rank multi-stage nested Wiener filter （MSNWF） based on subspace decomposition and Wiener filter （WF） to eliminate the effect of jamming in anti-jamming GPS receivers. A general sidelobe canceller （GSC） structure that is equivalent to the MSNWF is used to facilitate calculation of the optimal weights for the space-time processing. Simulation results demonstrate the satisfactory performance of the MSNWF to cancel jamming and the significant reduction in computational complexity by the reduced-rank processing. The technique offers a feasible space-time processing solution for anti-jamming GPS receivers.展开更多
Rank Histograms are suitable tools to assess the quality of ensembles within an ensemble prediction system or framework. By counting the rank of a given variable in the ensemble, we are basically making a sample analy...Rank Histograms are suitable tools to assess the quality of ensembles within an ensemble prediction system or framework. By counting the rank of a given variable in the ensemble, we are basically making a sample analysis, which does not allow us to distinguish if the origin of its variability is external noise or comes from chaotic sources. The recently introduced Mean to Variance Logarithmic （MVL） Diagram accounts for the spatial variability, being very sensitive to the spatial localization produced by infinitesimal perturbations of spatiotemporal chaotic systems. By using as a benchmark a simple model subject to noise, we show the distinct information given by Rank Histograms and MVL Diagrams. Hence, the main effects of the external noise can be visualized in a graphic. From the MVL diagram we clearly observe a reduction of the amplitude growth rate and of the spatial localization （chaos suppression）, while from the Rank Histogram we observe changes in the reliability of the ensemble. We conclude that in a complex framework including spatiotemporal chaos and noise, both provide a more complete forecasting picture.展开更多
文摘A number of previous papers have studied the problem of recovering low-rank matrices with noise, further combining the noisy and perturbed cases, we propose a nonconvex Schatten p-norm minimization method to deal with the recovery of fully perturbed low-rank matrices. By utilizing the p-null space property (p-NSP) and the p-restricted isometry property (p-RIP) of the matrix, sufficient conditions to ensure that the stable and accurate reconstruction for low-rank matrix in the case of full perturbation are derived, and two upper bound recovery error estimation ns are given. These estimations are characterized by two vital aspects, one involving the best r-approximation error and the other concerning the overall noise. Specifically, this paper obtains two new error upper bounds based on the fact that p-RIP and p-NSP are able to recover accurately and stably low-rank matrix, and to some extent improve the conditions corresponding to RIP.
文摘In a jamming environment with multiple wideband and narrowband jammers, global positioning system （GPS） receivers can use space-time processing to efficiently suppress the jamming. However, the computational complexity of space-time algorithms restricts their application in practical GPS receivers. This paper describes a reduced-rank multi-stage nested Wiener filter （MSNWF） based on subspace decomposition and Wiener filter （WF） to eliminate the effect of jamming in anti-jamming GPS receivers. A general sidelobe canceller （GSC） structure that is equivalent to the MSNWF is used to facilitate calculation of the optimal weights for the space-time processing. Simulation results demonstrate the satisfactory performance of the MSNWF to cancel jamming and the significant reduction in computational complexity by the reduced-rank processing. The technique offers a feasible space-time processing solution for anti-jamming GPS receivers.
基金support from MEC,Spain,through Grant No.CGL2007-64387/CLIthe AECID,Spain,for support through projects A/013666/07 and A/018685/08
文摘Rank Histograms are suitable tools to assess the quality of ensembles within an ensemble prediction system or framework. By counting the rank of a given variable in the ensemble, we are basically making a sample analysis, which does not allow us to distinguish if the origin of its variability is external noise or comes from chaotic sources. The recently introduced Mean to Variance Logarithmic （MVL） Diagram accounts for the spatial variability, being very sensitive to the spatial localization produced by infinitesimal perturbations of spatiotemporal chaotic systems. By using as a benchmark a simple model subject to noise, we show the distinct information given by Rank Histograms and MVL Diagrams. Hence, the main effects of the external noise can be visualized in a graphic. From the MVL diagram we clearly observe a reduction of the amplitude growth rate and of the spatial localization （chaos suppression）, while from the Rank Histogram we observe changes in the reliability of the ensemble. We conclude that in a complex framework including spatiotemporal chaos and noise, both provide a more complete forecasting picture.
