Reduced-dimension Root-MUSIC Algorithm Based on Spectral Factorization

YAN Fenggang; LIU Qiuchen; SHAO Duo; WANG Jun; WANG Kun; JIN Ming

doi:10.11999/JEIT170024

Volume 39 Issue 10

Oct. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(10): 2421-2427

YAN Fenggang, LIU Qiuchen, SHAO Duo, WANG Jun, WANG Kun, JIN Ming. Reduced-dimension Root-MUSIC Algorithm Based on Spectral Factorization[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2421-2427. doi: 10.11999/JEIT170024

Citation:

YAN Fenggang, LIU Qiuchen, SHAO Duo, WANG Jun, WANG Kun, JIN Ming. Reduced-dimension Root-MUSIC Algorithm Based on Spectral Factorization[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2421-2427. doi: 10.11999/JEIT170024

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Reduced-dimension Root-MUSIC Algorithm Based on Spectral Factorization

doi: 10.11999/JEIT170024

1.
(Harbin Institute of Technology at Weihai, Weihai 264209, China)
2.
(Xidian University, Xi&rsquo

Funds:

The National Natural Science Foundation of China (61501142), China Postdoctoral Science Foundation (2015M571414), Science and Technology Program of Weihai and Project Supported by Discipline Construction Guiding Foundation in Harbin Institute of Technology (Weihai) (WH20160107), The Fundamental Research Funds for the Central Universities (HIT.NSRIF.201725)

Received Date: 2017-01-09
Rev Recd Date: 2017-05-22
Publish Date: 2017-10-19

Abstract

Abstract

The Root MUltiple SIgnal Classification (Root-MUSIC) algorithm uses polynomial rooting instead of spectral search to reduce the computational complexity of Direction-Of-Arrival (DOA) estimation. However, when large numbers of sensors are exploited, this algorithm is still time-consuming. To further reduce the complexity, a novel Reduced-Dimension Root-MUSIC (RD-Root-MUSIC) algorithm based on spectral factorization is proposed, in which the dimension of polynomial involved in the rooting step is efficiently reduced to half. A companion matrix whose eigenvalues correspond to the roots of the reduced-dimension polynomial is further constructed, and the Arnoldi iteration is finally used to calculate only the L largest eigenvalues containing DOA information, where L is the number of signals. Simulation results show that RD-Root-MUSIC has a similar performance with much lower complexity as compared to Root-MUSIC.

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References(23)

References

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