In this section
@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}
}
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 -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom