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

WU Yuntao; HUANG Longting; CAO Hui; ZHANG Yanbin

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(4): 729-734

WU Yuntao, HUANG Longting, CAO Hui, et al., “HOSVD-Based Subspace Algorithm for Multidimensional Frequency Estimation Without Pairing Parameters,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 729-734, 2014,

Citation:

WU Yuntao, HUANG Longting, CAO Hui, et al., “HOSVD-Based Subspace Algorithm for Multidimensional Frequency Estimation Without Pairing Parameters,” Chinese Journal of Electronics, vol. 23, no. 4, pp. 729-734, 2014,

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

1.
Key Laboratory of Intelligent Robot in Hubei Province, Wuhan Institute of Technology, Wuhan 430073, China;
2.
Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China;
3.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61172150), the program for New Century Excellent Talents in University (No.NCET-13-0940) and the Research Plan Project of Hubei Provincial Department of Education (No.T201206).

Received Date: 2012-09-01
Rev Recd Date: 2013-10-01
Publish Date: 2014-10-05

Abstract

Abstract

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.
- Multidimensional frequency estimation,
- Higher-order singular value decomposition,
- Subspace-based method,
- Projection separation approach

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References(18)

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