Robust direction of arrival (DOA) estimation using RBF neural network in impulsive noise environment

  • Authors:

  • Hong Tang;Tianshuang Qiu;Sen Li;Ying Guo;Wenrong Zhang

  • Affiliations:

  • School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China;School of Electronic and Information Engineering, Dalian University of Technology, Dalian, Liaoning, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2005

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Abstract

The DOA problem in impulsive noise environment is approached as a mapping which can be modeled using a radial-basis function neural network (RBFNN). To improve the robustness, the input pairs are preprocessed by Fractional Low-Order Statistics (FLOS) technique. The performance of this network is compared to that of the FLOM-MUSIC for both uncorrelated and correlated source. Numerical results show the good performance of the RBFNN-based DOA estimation.