JSW 2012 Vol.7(12): 2710-2716 ISSN: 1796-217X
doi: 10.4304//jsw.7.12.2710-2716
doi: 10.4304//jsw.7.12.2710-2716
The Central DOA Estimation Algorithm Based on Support Vector Regression for Coherently Distributed Source
Yinghua Han, Jinkuan Wang
Northeastern University at Qinhuangdao, Qinhuangdao, China
Abstract—In this paper, the problem of estimating the central direction of arrival (DOA) of coherently distributed source impinging upon a uniform linear array is considered. An efficient method based on the support vector regression is proposed. After a training phase in which several known input/output mapping are used to determine the parameters of the support vector machines, among the outputs of the array and the central DOA of unknown plane waves is approximated by means of a family of support vector machines. So they perform well in response to input signals that have not been initially included in the training set. Furthermore, particle swarm optimization (PSO) algorithm is expressed for determination of the support vector machine parameters, which is very crucial for its learning results and generalization ability. Several numeral results are provided for the validation of the proposed approach.
Index Terms—coherently distributed source; the central DOA; angular spread; support vector machines; particle swarm optimization
Abstract—In this paper, the problem of estimating the central direction of arrival (DOA) of coherently distributed source impinging upon a uniform linear array is considered. An efficient method based on the support vector regression is proposed. After a training phase in which several known input/output mapping are used to determine the parameters of the support vector machines, among the outputs of the array and the central DOA of unknown plane waves is approximated by means of a family of support vector machines. So they perform well in response to input signals that have not been initially included in the training set. Furthermore, particle swarm optimization (PSO) algorithm is expressed for determination of the support vector machine parameters, which is very crucial for its learning results and generalization ability. Several numeral results are provided for the validation of the proposed approach.
Index Terms—coherently distributed source; the central DOA; angular spread; support vector machines; particle swarm optimization
Cite: Yinghua Han, Jinkuan Wang, "The Central DOA Estimation Algorithm Based on Support Vector Regression for Coherently Distributed Source," Journal of Software vol. 7, no. 12, pp. 2710-2716, 2012.
General Information
ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
-
Apr 26, 2021 News!
Vol 14, No 4- Vol 14, No 12 has been indexed by IET-(Inspec) [Click]
-
Nov 18, 2021 News!
Papers published in JSW Vol 16, No 1- Vol 16, No 6 have been indexed by DBLP [Click]
-
Dec 24, 2021 News!
Vol 15, No 1- Vol 15, No 6 has been indexed by IET-(Inspec) [Click]
-
Nov 18, 2021 News!
[CFP] 2022 the annual meeting of JSW Editorial Board, ICCSM 2022, will be held in Rome, Italy, July 21-23, 2022 [Click]
-
Aug 01, 2023 News!