Fast and Robust RBF Neural Network Based on Global K-Means Clustering With Adaptive Selection Radius for Sound Source Angle Estimation
Fast and Robust RBF Neural Network Based on Global K-Means Clustering With Adaptive Selection Radius for Sound Source Angle Estimation
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DOI：
10.1109/tap.2018.2823713
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发表时间：
2018-04
影响因子：
5.7
通讯作者：
Xiaopeng Yang;Yuqing Li;Yuze Sun;T. Long;T. Sarkar
中科院分区：
文献类型：
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作者：
Xiaopeng Yang;Yuqing Li;Yuze Sun;T. Long;T. Sarkar
声源定位技术在目标检测与定位中有着广泛的应用。然而，传统的声源定位方法由于估计精度、计算复杂度以及环境的灵活性等原因，在实际环境中的应用受到限制。为了提高实际环境中声源定位的性能，提出了一种基于全局K均值聚类的自适应选择半径的快速鲁棒径向基函数（RBF）神经网络。该方法根据种群密度抽样方法计算自适应选择半径，在全局K均值聚类过程中去除聚类中心周围不必要的点，与传统神经网络相比，可实现隐层神经元参数的快速优化.然后，通过求解到达时间差和声源定位的非线性方程组，训练RBF神经网络进行声源定位。由于在全局K均值聚类中采用了自适应的选择半径，因此该方法可以在较低的计算复杂度下获得较好的性能。基于仿真和实际实验数据，对所提出的方法进行了验证，并与传统的神经网络声源定位方法进行了比较。
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.