An Optimum RBF Network for Signal Detection in Non-Gaussian Noise
In this paper, we propose a radial basis function (RBF) neural network for detecting a known signal in the presence of non-Gaussian and Gaussian noise. In case of non-Gaussian noise, our study shows that RBF signal detector has significant improvement in performance characteristics; detection capability is better to those obtained with multilayer perceptrons (MLP) and the matched filter (MF) detector.
KeywordsRadial Basis Function Signal Detection Radial Basis Function Neural Network Radial Basis Function Network Matched Filter
- 2.Kassam, S.A.: Signal Detection in Non-Gaussian Noise. Springer, Heidelberg (1988)Google Scholar
- 4.Lippmann, R.P., Beckman, P.: Adaptive neural net preprocessing for signal detection in non-Gaussian noise. In: Advances in Neural Information Processing Systems, vol. 1 (1989)Google Scholar
- 5.Michalopoulou, Z., Nolta, L., Alexandrou, D.: Performance evaluation of multilayer perceptrons in signal detection and classification. IEEE Transactions on Neural Networks 6(2) (1995)Google Scholar
- 6.Gandhi, P.P., Ramamurti, V.: Neural networks for signal detection in non-Gaussian noise. IEEE Transactions on Signal Processing 45(11) (1997)Google Scholar