¹Department of Electrical Engineering, Indian Institute of Technology, Bombay

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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.

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Cite: D. G. Khairnar, S. N. Merchant, U. B. Desai, "Radar Signal Detection In Non-Gaussian Noise Using RBF Neural Network," Journal of Computers vol. 3, no. 1, pp. 32-39, 2008.