Imperial College London
Alternate

DrAlastairMoore

Faculty of Engineering, Department of Electrical and Electronic Engineering

Research Fellow in Acoustic Signal Processing
 
 
 
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Contact

 

alastair.h.moore

 
 
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Location

 

809Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Hafezi:2017:10.1109/HSCMA.2017.7895566,
author = {Hafezi, S and Moore, AH and Naylor, PA},
doi = {10.1109/HSCMA.2017.7895566},
pages = {81--85},
publisher = {IEEE},
title = {Multi-source estimation consistency for improved multiple direction-of-arrival estimation},
url = {http://dx.doi.org/10.1109/HSCMA.2017.7895566},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In Direction-of-Arrival (DOA) estimation for multiple sources, removal of noisy data points from a set of local DOA estimates increases the resulting estimation accuracy, especially when there are many sources and they have small angular separation. In this work, we propose a post-processing technique for the enhancement of DOA extraction from a set of local estimates using the consistency of these estimates within the time frame based on adaptive multi-source assumption. Simulations in a realistic reverberant environment with sensor noise and up to 5 sources demonstrate that the proposed technique outperforms the baseline and state-of-the-art approaches. In these tests the proposed technique had the worst average error of 9°, robustness of 5° to widely varying source separation and 3° to number of sources.
AU  - Hafezi,S
AU  - Moore,AH
AU  - Naylor,PA
DO  - 10.1109/HSCMA.2017.7895566
EP  - 85
PB  - IEEE
PY  - 2017///
SP  - 81
TI  - Multi-source estimation consistency for improved multiple direction-of-arrival estimation
UR  - http://dx.doi.org/10.1109/HSCMA.2017.7895566
UR  - http://hdl.handle.net/10044/1/45039
ER  -
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