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计算机工程 ›› 2011, Vol. 37 ›› Issue (17): 155-157. doi: 10.3969/j.issn.1000-3428.2011.17.052

• 人工智能及识别技术 • 上一篇    下一篇

一种改进的RBF神经网络DOA估计方法

巫军卫,张 旻,钟子发   

  1. (解放军电子工程学院信息工程系安徽省电子制约技术重点实验室,合肥 230037)
  • 收稿日期:2011-02-25 出版日期:2011-09-05 发布日期:2011-09-05
  • 作者简介:巫军卫(1987-),男,硕士研究生,主研方向:智能测向,神经网络;张 旻,教授、博士;钟子发,教授、博士生导师
  • 基金资助:
    国家自然科学基金资助项目(60972161);国家部委预研基金资助项目

Improved DOA Estimation Method for RBF Neural Network

WU Jun-wei, ZHANG Min, ZHONG Zi-fa   

  1. (Anhui Province Key Laboratory of Electronic Restriction, Department of Information Engineering, Electronic Engineering Institute of PLA, Hefei 230037, China)
  • Received:2011-02-25 Online:2011-09-05 Published:2011-09-05

摘要: 提出一种优化径向基函数神经网络来波方位(DOA)估计模型结构和参数的方法。利用误差准则函数的收敛性,合理确定模型的隐层神经元数目,根据阵列信号相位差特征的空间分布特点,选择具有代表性的隐层神经元的中心,构建的RBF神经网络更能反映阵列的测向能力。相比于目前的径向基函数神经网络测向模型的构建方法,改进的DOA估计模型具有更好的泛化性能,能够提高测向精度。实验结果验证了该方法的有效性。

关键词: 来波方位, 径向基函数神经网络, 误差准则函数, 隐层神经元, 初始中心

Abstract: A novel algorithm for optimizing the structure and parameters of Direction of Arrival(DOA) estimation model based on radial basis function neural network is presented. By using the astringency of error criteria function, the number of hidden neurons can be decided reasonably, according to the distribution of signal phase difference between the antenna array, the representative hidden neuron centers can be selected. By this way, the constructed RBF model can be represented the direction finding capacity of the antenna array. Compared with the other Radial Basis Function(RBF) methods, the proposed model has the features of more generalization and accuracy DOA estimation. Experimental results show the effectiveness of the proposed approach.

Key words: direction of arrival wave, Radial Basis Function(RBF) neural network, error criteria function, hidden neuron, initial center

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