Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and...Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and mitigating the negative influences of the Gaussian colored noise. However, in the presence of unexpected modeling errors, the resolution behavior of the FOC-RARE also deteriorate significantly as SOS-RARE, even for a known array covariance matrix. For this reason, the angle resolution capability of the FOC-RARE was theoretically analyzed. Firstly, the explicit formula for the mathematical expectation of the FOC-RARE spatial spectrum was derived through the second-order perturbation analysis method. Then, with the assumption that the unexpected modeling errors were drawn from complex circular Gaussian distribution, the theoretical formulas for the angle resolution probability of the FOC-RARE were presented. Numerical experiments validate our analytical results and demonstrate that the FOC-RARE has higher robustness to the unexpected modeling en＇ors than that of the SOS-RARE from the resolution point of view.展开更多
Firstly, this article carry on the theory research combined with the related theories of cluster brand and the relevant theories of performance evaluation, and distill four main indicators factors of performance evalu...Firstly, this article carry on the theory research combined with the related theories of cluster brand and the relevant theories of performance evaluation, and distill four main indicators factors of performance evaluation of the cluster brand. Then proceed analysts survey and questionnaire investigation, and then use SPSS software to process date for statistical analysis; use AMOS software to formulate the second-order model, at last, establish structure relationship model of performance evaluation of the cluster brand. Build the model of the cluster brand performance evaluation with fuzzy-synthetic evaluation model.展开更多
基金Project(61201381)supported by the National Nature Science Foundation of ChinaProject(YP12JJ202057)supported by the Future Development Foundation of Zhengzhou Information Science and Technology College,China
文摘Compared to the rank reduction estimator （RARE） based on second-order statistics （called SOS-RARE）, the RARE employing fourth-order cumulants （referred to as FOC-RARE） is capable of dealing with more sources and mitigating the negative influences of the Gaussian colored noise. However, in the presence of unexpected modeling errors, the resolution behavior of the FOC-RARE also deteriorate significantly as SOS-RARE, even for a known array covariance matrix. For this reason, the angle resolution capability of the FOC-RARE was theoretically analyzed. Firstly, the explicit formula for the mathematical expectation of the FOC-RARE spatial spectrum was derived through the second-order perturbation analysis method. Then, with the assumption that the unexpected modeling errors were drawn from complex circular Gaussian distribution, the theoretical formulas for the angle resolution probability of the FOC-RARE were presented. Numerical experiments validate our analytical results and demonstrate that the FOC-RARE has higher robustness to the unexpected modeling en＇ors than that of the SOS-RARE from the resolution point of view.
文摘Firstly, this article carry on the theory research combined with the related theories of cluster brand and the relevant theories of performance evaluation, and distill four main indicators factors of performance evaluation of the cluster brand. Then proceed analysts survey and questionnaire investigation, and then use SPSS software to process date for statistical analysis; use AMOS software to formulate the second-order model, at last, establish structure relationship model of performance evaluation of the cluster brand. Build the model of the cluster brand performance evaluation with fuzzy-synthetic evaluation model.
