DOA Finding with Support Vector Regression Based Forward-Backward Linear Prediction

被引：9

作者：

Pan, Jingjing ^{[1
]}

Wang, Yide ^{[1
]}

Le Bastard, Cedric ^{[1
,2
]}

Wang, Tianzhen ^{[3
]}

机构：

[1] Polytech Nantes, CNRS, Inst Elect & Telecommun Rennes, UMR 6164, Rue Christian Pauc,BP 50609, F-44306 Nantes 3, France

[2] Cerema, F-49136 Les Ponts De Ce, France

[3] Shanghai Maritime Univ, Logist Engn Coll, Shanghai 201306, Peoples R China

来源：

SENSORS | 2017年 / 17卷 / 06期

基金：

中国国家自然科学基金;

关键词：

direction-of-arrival (DOA); support vector regression (SVR); forward-backward linear prediction (FBLP); coherent signals; low snapshots; SPATIAL SMOOTHING TECHNIQUES; OF-ARRIVAL ESTIMATION; MULTIPATH; SYSTEM;

D O I：

10.3390/s17061225

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward-backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method.

引用

页数：11

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