A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors

  • Authors:

  • Bin Li;Zheng Zhou;Dejian Li;Weixia Zou

  • Affiliations:

  • Key Lab of Universal Wireless Communications, MOE, Wireless Network Laboratory, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China;Key Lab of Universal Wireless Communications, MOE, Wireless Network Laboratory, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China;Key Lab of Universal Wireless Communications, MOE, Wireless Network Laboratory, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China;Key Lab of Universal Wireless Communications, MOE, Wireless Network Laboratory, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China

  • Venue:
  • Journal of Network and Computer Applications
  • Year:
  • 2011

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Abstract

Ultra-wideband (UWB) has been widely recommended for significant commercial and military applications. However, the well-derived coherent structures for UWB signal detection are either computationally complex or hardware impractical in the presence of the intensive multipath propagations. In this article, based on the nonparametric Parzen window estimator and the probabilistic neural networks, we suggest a low-complexity and noncoherent UWB detector in the context of distributed wireless sensor networks (WSNs). A novel characteristic spectrum is firstly developed through a sequence of blind signal transforms. Then, from a pattern recognition perspective, four features are extracted from it to fully exploit the inherent property of UWB multipath signals. The established feature space is further mapped into a two-dimensional plane by feature combination in order to simplify algorithm complexity. Consequently, UWB signal detection is formulated to recognize the received patterns in this formed 2-D feature plane. With the excellent capability of fast convergence and parallel implementation, the Parzen Probabilistic Neural Network (PPNN) is introduced to estimate a posteriori probability of the developed patterns. Based on the underlying Bayesian rule of PPNN, the asymptotical optimal decision bound is finally determined in the feature plane. Numerical simulations also validate the advantages of our proposed algorithm.