JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 3    Issue : 1    Date : January 2008

Radar Signal Detection In Non-Gaussian Noise Using RBF Neural Network
D. G. Khairnar, S. N. Merchant, and Uday B. Desai
Page(s): 32-39
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Abstract
In this paper, we suggest a neural network signal detector using radial basis function (RBF)
network. We employ this RBF Neural detector to detect the presence or absence of a known signal
corrupted by different Gaussian, non-Gaussian and impulsive noise components. In case of
non-Gaussian noise, experimental results show that RBF network signal detector has significant
improvement in performance characteristics. Detection capability is better than to those obtained
with multilayer perceptrons (BP) and optimum matched filter (MF) detector. This signal detector is
also tested on the simulated signals impacted by impulsive noise produced by atmospheric events
and short lived echoes from meteor trains. Tested Results show, improved detection capability to
impulsive noise compare to BP signal detector. It also show better performance as a function of
signal-tonoise ratio compared to BP and MF.

Index Terms
Radial basis function neural network, non- Gaussian noise, impulsive noise, signal detection.