TY - JOUR
T1 - Fast and Robust RBF Neural Network Based on Global K-Means Clustering with Adaptive Selection Radius for Sound Source Angle Estimation
AU - Yang, Xiaopeng
AU - Li, Yuqing
AU - Sun, Yuze
AU - Long, Teng
AU - Sarkar, Tapan K.
N1 - Funding Information:
Manuscript received August 13, 2017; revised January 10, 2018; accepted March 21, 2018. Date of publication April 11, 2018; date of current version May 31, 2018. This work was supported in part by 111 Project of China under Grant B14010 and in part by the National Natural Science Foundation of China under Grant 61671065 and Grant 31727901. (Corresponding author: Xiaopeng Yang.) X. Yang, Y. Li, Y. Sun, and T. Long are with the School of Information and Electronics, and also with the Key Laboratory of Electronic and Information Technology in Satellite Navigation, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China (e-mail: xiaopengyang@bit.edu.cn).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2018/6
Y1 - 2018/6
N2 - The sound source localization technique is widely applied to target detection and localization. However, the application of conventional sound source localization methods is limited in actual environment because of estimation accuracy, computational complexity, and flexibility of the environment. In order to improve the sound source localization performance in actual environment, a fast and robust radial basis function (RBF) neural network based on global K-means clustering with adaptive selection radius is proposed in this paper. In the proposed method, an adaptive selection radius is calculated according to the population density sampling method to remove unnecessary points around cluster centers during the global K-means clustering; thus, compared with the conventional neural network, a fast optimization of hidden layer neuron parameters can be achieved. Afterward, the RBF neural network is trained to locate the sound source by solving nonlinear equations of the time difference of arrival and sound source location. Because of the adoption of adaptive selection radius in global K-means clustering, the proposed method can provide desirable performance with low computational complexity. Based on the simulated and actual experimental data, the proposed method is verified and an improved performance is achieved compared with that of conventional neural network sound source localization methods.
AB - The sound source localization technique is widely applied to target detection and localization. However, the application of conventional sound source localization methods is limited in actual environment because of estimation accuracy, computational complexity, and flexibility of the environment. In order to improve the sound source localization performance in actual environment, a fast and robust radial basis function (RBF) neural network based on global K-means clustering with adaptive selection radius is proposed in this paper. In the proposed method, an adaptive selection radius is calculated according to the population density sampling method to remove unnecessary points around cluster centers during the global K-means clustering; thus, compared with the conventional neural network, a fast optimization of hidden layer neuron parameters can be achieved. Afterward, the RBF neural network is trained to locate the sound source by solving nonlinear equations of the time difference of arrival and sound source location. Because of the adoption of adaptive selection radius in global K-means clustering, the proposed method can provide desirable performance with low computational complexity. Based on the simulated and actual experimental data, the proposed method is verified and an improved performance is achieved compared with that of conventional neural network sound source localization methods.
KW - Adaptive selection radius
KW - global K-means clustering
KW - radial basis function (RBF) neural network
KW - sound source localization
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U2 - 10.1109/TAP.2018.2823713
DO - 10.1109/TAP.2018.2823713
M3 - Article
AN - SCOPUS:85045321620
SN - 0018-926X
VL - 66
SP - 3097
EP - 3107
JO - IEEE Transactions on Antennas and Propagation
JF - IEEE Transactions on Antennas and Propagation
IS - 6
ER -