TY - JOUR
T1 - Improving direction of arrival estimation based on directivity pattern analysis and adaptive cascaded classifiers
AU - Chen, Bo Wei
AU - Wang, Jhing Fa
AU - Wang, Jia Ching
N1 - Funding Information:
This work was supported in part by the National Science Council of the Republic of China under Grant NSC97-2221-E-006-249-MY3.
PY - 2010
Y1 - 2010
N2 - Direction of arrival (DOA) estimation via directivity pattern analysis (DPA) has been proposed for years, in order to locate signal sources. It uses directional nulls existing in directivity patterns to approximate angles. However, traditional directivity pattern analysis often causes biased estimation, which is usually derived from ambiguous patterns. In this study, we convert the DOA problem from the signal domain into the visual domain, so that pattern analyses and recognition techniques are applicable. A novel architecture, composed of adaptive cascaded separators and a neural network, is presented to minimize the effects of obscure directional nulls. We also employ an adaptive algorithm for collecting the refined information generated by the neural network and updating the separators automatically. The experimental results show that this system is less susceptible to the effects of inappropriate patterns than other systems. Simulations were performed to compare results between the conventional approaches and our proposed method.
AB - Direction of arrival (DOA) estimation via directivity pattern analysis (DPA) has been proposed for years, in order to locate signal sources. It uses directional nulls existing in directivity patterns to approximate angles. However, traditional directivity pattern analysis often causes biased estimation, which is usually derived from ambiguous patterns. In this study, we convert the DOA problem from the signal domain into the visual domain, so that pattern analyses and recognition techniques are applicable. A novel architecture, composed of adaptive cascaded separators and a neural network, is presented to minimize the effects of obscure directional nulls. We also employ an adaptive algorithm for collecting the refined information generated by the neural network and updating the separators automatically. The experimental results show that this system is less susceptible to the effects of inappropriate patterns than other systems. Simulations were performed to compare results between the conventional approaches and our proposed method.
KW - Adaptive algorithm
KW - Direction of arrival (DOA)
KW - Directivity pattern analysis
KW - Sound localization
UR - http://www.scopus.com/inward/record.url?scp=77958092271&partnerID=8YFLogxK
U2 - 10.1080/02533839.2010.9671664
DO - 10.1080/02533839.2010.9671664
M3 - 期刊論文
AN - SCOPUS:77958092271
SN - 0253-3839
VL - 33
SP - 751
EP - 760
JO - Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
JF - Journal of the Chinese Institute of Engineers, Transactions of the Chinese Institute of Engineers,Series A/Chung-kuo Kung Ch'eng Hsuch K'an
IS - 5
ER -