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基于譜分解的降階求根MUSIC算法

閆鋒剛,  劉秋晨,  邵多,  王軍,  王坤,  金銘

閆鋒剛, 劉秋晨, 邵多, 王軍, 王坤, 金銘. 基于譜分解的降階求根MUSIC算法[J]. 電子與信息學(xué)報(bào), 2017, 39(10): 2421-2427. doi: 10.11999/JEIT170024
引用本文: 閆鋒剛, 劉秋晨, 邵多, 王軍, 王坤, 金銘. 基于譜分解的降階求根MUSIC算法[J]. 電子與信息學(xué)報(bào), 2017, 39(10): 2421-2427. doi: 10.11999/JEIT170024
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

基于譜分解的降階求根MUSIC算法

doi: 10.11999/JEIT170024
  • 1.

    (哈爾濱工業(yè)大學(xué)(威海) 威海 264209) ②(西安電子科技大學(xué) 西安 710071) ③(中國(guó)人民解放軍63891部隊(duì) 洛陽(yáng) 471023)

基金項(xiàng)目: 

國(guó)家自然科學(xué)基金(61501142)，中國(guó)博士后科學(xué)基金 (2015M571414)，威海市科技攻關(guān)和哈爾濱工業(yè)大學(xué)(威海)學(xué)科建設(shè)引導(dǎo)基金(WH20160107)，中央高?；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(HIT.NSRIF.201725)

Reduced-dimension Root-MUSIC Algorithm Based on Spectral Factorization

  • 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)

  • 摘要: 求根多重信號(hào)分類(Root-MUSIC)算法以多項(xiàng)式求根代替譜峰搜索，降低了波達(dá)方向(DOA)估計(jì)的計(jì)算量，但當(dāng)陣元數(shù)較大時(shí)，其計(jì)算量依然很大。為進(jìn)一步降低計(jì)算量，該文提出一種降階Root-MUSIC(RD-Root-MUSIC)算法。該算法基于譜分解將Root-MUSIC多項(xiàng)式的階次降低一半，再根據(jù)矩陣特征多項(xiàng)式與求根多項(xiàng)式的關(guān)系構(gòu)造友陣，采用Arnoldi迭代計(jì)算得到友陣的L個(gè)大特征值(L為信號(hào)數(shù))并估計(jì)DOA。仿真結(jié)果表明，RD-Root-MUSIC估計(jì)精度與Root-MUSIC相近，但其在大陣元下具有比Root-MUSIC更低的計(jì)算量。
    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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出版歷程
  • 收稿日期:  2017-01-09
  • 修回日期:  2017-05-22
  • 刊出日期:  2017-10-19

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