Name
Abstract | ||
|---|---|---|
A method is presented for estimating direction of arrival (DOA) of multiple spatio-temporal sources in the domain, which is based on independent component analysis (ICA). Firstly, original time-domain data is dimensionally reduced by using some classical second-order techniques for estimating number of independent sources (NIS), such as singular value decomposition (SVD) and eigenvalue decomposition (EVD). From every reduced data segment, steering vectors of the mixing system from sources to sensors are directly identified by some instantaneous ICA algorithms. Furthermore, DOAs of independent sources are captured by using a whole estimating strategy. Consequently, all time-domain data segments contribute to a final DOA estimation set, in which every source direction is shown as a direction cluster and/or local maximum. Experimental results indicate potential applicability of the proposed ICA based methods. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICNC.2009.28 | International Conference on Natural Computation |
Keywords | Field | DocType |
original time-domain data,time domain,multiple spatio-temporal sources,doa estimation,multiple signal classification (music),steering vectors,whole estimating strategy,proposed ica,whole estimation strategy,classical second-order techniques,reduced data segment,steering identification,dimensional redundancy,instantaneous ica algorithms,number of independent sources (nis),time domain data analysis,music,direction of arrival estimation,independent component analysis,independent component analysis (ica),source direction,time-domain analysis,independent source,direction-of-arrival estimation,signal classification,instantaneous ica algorithm,eigenvalue decomposition,direction cluster,time-domain data segment,singular value decomposition,direction of arrival (doa),multiple signal classification,matrix decomposition,sensors,estimation | Time domain,Singular value decomposition,Data segment,Multiple signal classification,Pattern recognition,Computer science,Direction of arrival,Matrix decomposition,Independent component analysis,Eigendecomposition of a matrix,Artificial intelligence | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3736-8 | 1 |
PageRank | References | Authors |
0.36 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiao Weidong | 1 | 1 | 0.36 |
| Chang Yong | 2 | 1 | 0.36 |