Direction of Arrival Estimation Approach Based on Phase Angle Feature of Correlation Function Using RBF Neural Networks

Zhang Min; Li Peng-fei

doi:10.3724/SP.J.1146.2008.01677

Volume 31 Issue 12

Dec. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2009 > 31(12): 2926-2930

Zhang Min, Li Peng-fei. Direction of Arrival Estimation Approach Based on Phase Angle Feature of Correlation Function Using RBF Neural Networks[J]. Journal of Electronics & Information Technology, 2009, 31(12): 2926-2930. doi: 10.3724/SP.J.1146.2008.01677

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Zhang Min, Li Peng-fei. Direction of Arrival Estimation Approach Based on Phase Angle Feature of Correlation Function Using RBF Neural Networks[J]. Journal of Electronics & Information Technology, 2009, 31(12): 2926-2930. doi: 10.3724/SP.J.1146.2008.01677

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Direction of Arrival Estimation Approach Based on Phase Angle Feature of Correlation Function Using RBF Neural Networks

doi: 10.3724/SP.J.1146.2008.01677

Received Date: 2008-12-10
Rev Recd Date: 2009-07-16
Publish Date: 2009-12-19

Abstract

Abstract

Effective feature extraction is very important when building the smart DOA estimation model. Based on analyzing the correlation function of the array signal, this paper firstly presents using the angles of contiguous array signals correlation function for DOA estimation purpose instead of common used upper triangular half of the covariance matrix, which eliminates the irrelevant magnitude information and redundant direction characteristic. Therefore the feature dimension is largely reduced without losing any DOA information. Experimental results show that the performance of RBF neural network using proposed Phase-feature is superior to the common used upper triangular half of the covariance matrix in terms of neural network size, generalization, estimation precision and real-time performance, so it has a broad application value.

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References(1)

References

安冬, 王守觉. 基于仿生模式识别的DOA 估计方法[J].电子与信息学报.2004, 26(9):1468-1472浏览[2]Guo Wo, Qiu T S, and Tang H, et al.. Performance of RBFneural networks for array processing in impulsive noiseenvironment [J].Digital SignalL Processing.2008, 18(2):168-178[3]Wang M, Yang S, and Wu S, et al.. A RBFNN approach forDOA estimation of ultra wideband antenna array[J].Neurocomputing. 2008, 71(4-6): 631-640.[4]Vigneshwaran S, Sundararajan Narasimhan, andSaratchandran P. Direction of arrival (DOA) estimationunder array sensor failures using a minimal resourseallocation neural network[J].IEEE Transactions on Antennasand Propagation.2007, 55(2):334-343[5]Dourado O D, Doria A D, and Da Mata W. Determination ofmultiple direction of arrival in antennas arrays with radialbasis functions[J].Neurocomputing.2006, 70(1-3):55-61[6]Kuwahra and Matsumoto. Experiments of direction finder byRBF neural network with post processing. IEEE ElectronicLetters, 2005, 41(10): 24-25.[7]严颂华, 吴世才, 吴雄斌. 基于神经网络的高频地波雷达目标到达角估计[J].电子与信息学报.2008, 30(2):339-342浏览[8]于斌, 尹成友, 黄冶. 阵列误差影响下的神经网络波达方向估计[J]. 微波学报, 2007, 23(6): 21-25.Yu Bin, Yin Cheng-you, and Huang Ye. Direction ofarrival(DOA) estimation for an array with errors using RBFneural network [J]. Journal of Microwaves, 2007, 23(6): 21-25.[9]安冬, 王守觉. 基于仿生模式识别和PCA/ICA 的DOA 估计方法[J]. 电子学报, 2004, 32(9): 1448-1451.An Dong and Wang Shou-jue. A DOA estimation methodbased on biomimetic pattern recognition and PCA/ICA [J].Acta Electronica Sinica, 2004, 32(9): 1448-1451.[10]Shieh Ching-Sung and Lin Chin-Teng. Direction of arrivalestimation based on phase differences using neural fuzzynetwork[J].IEEE Transactions on Antennas and Propagation.2000, 48(7):1115-1123[11]陈荆花, 易辉跃, 周希朗. 基于特征矢量输入的神经网络测向方法[J]. 上海交通大学学报, 2003, 37(3): 373-379.Chen Jing-hua, Yi Hui-yue, and Zhou Xi-lang. Direction ofarrival estimation method with eigenvector based radial basisfunction neural network[J]. Journal of Shanghai Jiao TongUniversity, 2003, 37(3): 373-379.

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