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| Reduced-dimension Root-MUSIC Algorithm Based on Spectral Factorization |
| YAN Fenggang① LIU Qiuchen① SHAO Duo② WANG Jun① WANG Kun③ JIN Ming① |
①(Harbin Institute of Technology at Weihai, Weihai 264209, China)
②(Xidian University, Xi’an 710071, China)
③(63891 Unit of Army, PLA, Luoyang 471023, China) |
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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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Received: 09 January 2017
Published: 27 June 2017
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| Fund: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) |
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Corresponding Authors:
WANG Jun
E-mail: hitwangjun@126.com
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