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Unitary root-MUSIC based on tensor mode-R algorithm for multidimensional sinusoidal frequency estimation without pairing parameters

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Abstract

In this paper, an algorithm of combing unitary Root-MUSIC method based on tensor mode-R and projection separation approach is proposed for multidimensional (R-D) sinusoidal parameters estimation. The model of the proposed algorithm based on tensor mode-R with the unitary matrices is firstly transferred into multiple single-sample models, and then the eigenvalue decomposition (EVD) or singular value decomposition (SVD) of a set of constructed covariance matrices is implemented to obtain the estimators of all dimensional parameters. Compared with other available methods, the computational complexity of the EVD or SVD is largely reduced due to using unitary matrices by way of transforming complex number into real-valued, moreover, the problem of parameter matching is solved by the application of the projection separation operator with tensor mode-R. Simulation results are given to demonstrate the advantage of the proposed method in terms of performance of parameter estimation as well as the computational load over several state-of-art algorithms.

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Acknowledgements

The work described in this paper was supported by a grant from the National Natural Science Foundation of China (Project No. 61771353).

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Correspondence to Yuntao Wu.

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Hu, C., Wu, Y., Huang, L. et al. Unitary root-MUSIC based on tensor mode-R algorithm for multidimensional sinusoidal frequency estimation without pairing parameters. Multidim Syst Sign Process 31, 491–501 (2020). https://doi.org/10.1007/s11045-019-00672-5

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  • DOI: https://doi.org/10.1007/s11045-019-00672-5

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