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Sensors 2017, 17(6), 1225; doi:10.3390/s17061225

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

1
Institut d’Electronique et Télécommunications de Rennes, UMR CNRS 6164, Polytech Nantes, Rue Christian Pauc, BP 50609, 44306 Nantes CEDEX 3, France
2
Cerema, 49136 Les Ponts de Cé, France
3
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
Academic Editors: Zhiguo Shi, Yujie Gu and Rongxing Lu
Received: 19 April 2017 / Revised: 22 May 2017 / Accepted: 24 May 2017 / Published: 27 May 2017
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Abstract

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. View Full-Text
Keywords: direction-of-arrival (DOA); support vector regression (SVR); forward–backward linear prediction (FBLP); coherent signals; low snapshots direction-of-arrival (DOA); support vector regression (SVR); forward–backward linear prediction (FBLP); coherent signals; low snapshots
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pan, J.; Wang, Y.; Le Bastard, C.; Wang, T. DOA Finding with Support Vector Regression Based Forward–Backward Linear Prediction. Sensors 2017, 17, 1225.

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