Abstract
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.
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References
M.D. Srinath, P.K. Rajasekaran, R. Viswanathan: Introduction to statistical signal processing with applications. Prentice-Hall, Inc., 1996.
V. Aloisio, A. di Vito, G. Galati: Optimum detection of moderately fluctuating radar targets. IEE Proceedings on Radar, Sonar and Navigation, Vol. 141, No. 3, pp. 164–170, June 1994.
A. di Vito, M. Naldi: Robustness of the likelihood ratio detector for moderately flutuating radar targets. IEE Proeedings on Radar, Sonar and Navigation, Vol. 146, No. 2, pp. 107–112, April 1999.
J. W. Watterson: An optimun multilayer perceptron neural receiver for signal detection. IEEE Trans. on Neural Networks, Vol. 1, No. 4, pp. 298–300, December 1990.
D.W. Ruck, S.K. Rogers, M. Kabrisky, M.E. Oxley, B.W. Suter: The multilayer perceptron as an aproximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks, Vol. 1, No. 4, pp. 296–298, April 1990.
E.A. Wan: Neural network classification: A bayesian interpretation. IEEE Transactions on Neural Networks, Vol. 1, No. 4, pp. 303–305, December 1990.
G. C. Orsay: A note on estimating false alarm rates via importante sampling. IEEE Transactions on Communications, Vol. 41, No. 9, pp. 1275–1277, September 1993.
J. Grajal, A. Asensio: Multiparametric importante sampling for simulation of radar systems. IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 1, pp. 123–137, January 1999.
J. L. Sanz-Gonzalez, D. Andina: Performance anlisis of neural network detectors by importante sampling techniques. Neural Processing Letters, No. 9, pp. 257–269, 1999.
G. Cybenko: Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, Vol. 2, pp. 303–314, 1989.
S. Haykin: Neural networks. A comprehensive foundation (second edition). Prentice-Hall Inc., 1999.
F. Schwenker, H.A. Kestler, G. Palm: Three learning phases for radial-basis-function networks. Neural Networks, Vol. 14, Issue 4–5, pp. 439–458, 2001.
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Jarabo-Amores, P., Gil-Pita, R., Rosa-Zurera, M., López-Ferreras, F. (2003). MLP and RBFN for detecting white gaussian signals in white gaussian interference. In: Mira, J., Álvarez, J.R. (eds) Artificial Neural Nets Problem Solving Methods. IWANN 2003. Lecture Notes in Computer Science, vol 2687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44869-1_100
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DOI: https://doi.org/10.1007/3-540-44869-1_100
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