Mode Subspace Projection of a Tensor for Multidimensional Harmonic Parameter Estimations
In Multidimensional Harmonic Retrieval (MHR) problems, it is understood that the multidimensional structure of the measurement data, with a tensor representation, can be exploited to improve the parameter estimation accuracy. In this paper, the modeℜ subspace of the tensor representation, based on... Full description
Journal Title:  IEEE Transactions on Signal Processing 01 June 2013, Vol.61(11), pp.30023014 
Main Author:  Yang Li 
Other Authors:  Jian Qiu Zhang 
Format:  Electronic Article 
Language: 
English 
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ID:  ISSN: 1053587X ; EISSN: 19410476 ; DOI: 10.1109/TSP.2013.2255044 
Link:  https://ieeexplore.ieee.org/document/6488880 
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recordid:  ieee_s6488880 
title:  Mode Subspace Projection of a Tensor for Multidimensional Harmonic Parameter Estimations 
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ispartof:  IEEE Transactions on Signal Processing, 01 June 2013, Vol.61(11), pp.30023014 
description:  In Multidimensional Harmonic Retrieval (MHR) problems, it is understood that the multidimensional structure of the measurement data, with a tensor representation, can be exploited to improve the parameter estimation accuracy. In this paper, the modeℜ subspace of the tensor representation, based on the general matricization of the tensor, is first defined. It is found that there is a subordinate relation among the different mode ℜ signal subspaces. As a result, the measurement tensor can be projected to the mode ℜ signal subspaces in a bottomup way, and the longvector signal subspace required by the many signal subspace based parameter estimation algorithms can be refined. As an example, a modeℜ projection based TensorESPRIT algorithm is presented. The reason why modeℜ subspace projections bring about performance improvement becomes obvious by the first order perturbation analyses. These analyses also generate two criteria on how the modeℜ subspace based projection technique should be carried out. Simulations are conducted to verify the effectiveness of the algorithm and the analytical results. 
language:  eng 
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identifier:  ISSN: 1053587X ; EISSN: 19410476 ; DOI: 10.1109/TSP.2013.2255044 
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