HOSVD-Based Subspace Algorithm for Multidimensional Frequency Estimation Without Pairing Parameters  

作　　者：WU Yuntao HUANG Longting CAO Hui ZHANG Yanbin 

机构地区：[1]Key Laboratory of Intelligent Robot in Hubei Province,Wuhan Institute of Technology [2]Department of Electronic Engineering, City University of Hong Kong [3]School of Information and Communication Engineering, Beijing University of Posts and Telecommunications

出　　处：《Chinese Journal of Electronics》2014年第4期729-734,共6页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61172150);the program for New Century Excellent Talents in University(No.NCET-13-0940);the Research Plan Project of Hubei Provincial Department of Education(No.T201206)

摘　　要：In this paper, a new method for multidimensional frequency estimation of multiple sinusoids that combines the HOSVD(Higher-order singular value decomposition) subspace and projection separation approaches is presented. Frequency parameters in the first dimension are obtained by using the signal subspace of the first dimension which is extracted by the HOSVD decomposition. Subsequently, a set of projection separation matrices is constructed to project the measure tensor and separate the components of the received tensor into single ones.And then, the signal subspace of each dimension of separated measure tensor are estimated by the HOSVD decomposition and the desired multidimensional frequency pairing are automatically obtained. Simulation results are included to demonstrate the advantage of the proposed method over two existing methods in terms of performance as well computational load.In this paper, a new method for multidimensional frequency estimation of multiple sinusoids that combines the HOSVD （Higher-order singular value decomposition） subspace and projection separation approaches is presented. Frequency parameters in the first dimension are obtained by using the signal subspace of the first dimension which is extracted by the HOSVD decomposition. Subsequently, a set of projection separation matrices is constructed to project the measure tensor and separate the components of the received tensor into single ones. And then, the signal subspace of each dimension of separated measure tensor are estimated by the HOSVD decomposition and the desired multidimensional frequency pairing are automatically obtained. Simulation results are included to demonstrate the advantage of the proposed method over two existing methods in terms of performance as well computational load.

关 键 词：Multidimensional frequency estimation Higher-order singular value decomposition Subspacebased method Projection separation approach. 

分 类 号：TN911.7[电子电信—通信与信息系统]