Multi-dimensional EEF Criterion for Source Number Estimation  

作　　者：HUANG Qinghua WANG Tong 

机构地区：[1]School of Communication and Information Engineering, Shanghai University [2]Institute of Computer and Information, Shanghai Second Polytechnic University

出　　处：《Chinese Journal of Electronics》2014年第3期503-507,共5页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61001160);Innovation Program of Shanghai Municipal Education Commission(No.12YZ023)

摘　　要：When the number of snapshots is smaller than the dimension of the measured data, Exponential embedded family （EEF） rule fails to choose the correct model order. It is modified in this paper to enumerate sources in three-dimensional space unlimited by the number of snap- shots. Then the modified EEF criterion is extended to R- D version for the multl-dimensional data model based on Higher-order singular value decomposition （HOSVD）. The R-D EEF criterion exploits the multi-dimenslonal structure of measurements and the eigenvalues of different mode sample covariance matrices jointly. It improves accuracy and robustness compared with the modified EEF criterion. Simulation results demonstrate the performance of the proposed enumerators.When the number of snapshots is smaller than the dimension of the measured data, Exponential embedded family(EEF) rule fails to choose the correct model order. It is modified in this paper to enumerate sources in three-dimensional space unlimited by the number of snapshots. Then the modified EEF criterion is extended to RD version for the multi-dimensional data model based on Higher-order singular value decomposition(HOSVD). The R-D EEF criterion exploits the multi-dimensional structure of measurements and the eigenvalues of different mode sample covariance matrices jointly. It improves accuracy and robustness compared with the modified EEF criterion. Simulation results demonstrate the performance of the proposed enumerators.

关 键 词：Source enumeration Exponential embed- ded family Multl-dimensional structure Tensor model. 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]