Ultra-wideband nearfield adaptive beamforming based on a RBF neural network

  • Authors:

  • Min Wang;Shuyuan Yang;Shunjun Wu

  • Affiliations:

  • National Lab. of Radar Signal Processing, Xidian University, Xi’an, Shaanxi, China;Institute of Intelligence Information Processing, Department of Electrical Engineering, Xidian University, Xi'an, Shaanxi, China;National Lab. of Radar Signal Processing, Xidian University, Xi’an, Shaanxi, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
  • Year:
  • 2005

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Abstract

An adaptive beamforming method based on radial-basis function (RBF) neural network is examined for ultra-wideband (UWB) array illuminated by nearfield source in this paper. An analysis of the principle of space-time processing employing Gaussian monocycle model as UWB signal is conducted. The nearfield regionally constrain of UWB beamformer is reflected by a set of samples exerted on neural network training sample space. The recursive least square algorithm has been used for network weights updating. It improves the robustness against large errors in distance and directions of arrival. The efficiency and feasibility of presented approach is proved through the experimental results.