MLP and RBFN for Detecting White Gaussian Signals in White Gaussian Interference

This paper deals with the application of Neural Networks to binary detection based on multiple observations. The problem of detecting a desired signal in Additive-White-Gaussian-Noise is considered, assuming that the desired signal samples are also gaussian, independent and identically distributed random variables. The test statistic is then the squared magnitude of the observation vector and the optimum boundary is a hyper-sphere in the input space. The dependence of the neural network detector on the Training-Signal-to-Noise-Ratio and the number of hidden units is studied. Results show that Radial Basis Function Networks are less dependent on the Training-Signal-to-Noise-Ratio and the number of hidden units than Multilayer Perceptrons, and approximate better the Neyman-Pearson detector.