Abstract
Neural networks (NNs) are proposed for approximating the Average Likelihood Ratio (ALR). The detection of gaussian targets with gaussian autocorrelation function and unknown one-lag correlation coefficient, ρ s , in Additive White Gaussian Noise (AWGN) is considered. After proving the low robustness of the likelihood ratio (LR) detector with respect to ρ s , the ALR detector assuming a uniform distribution of this parameter in [0,1] has been studied. Due to the complexity of the involved integral, two NN based solutions are proposed. Firstly, single Multi-Layer Perceptrons (MLPs) are trained with target patterns with ρ s varying in [0,1]. This scheme outperforms the LR detector designed for a fixed value of ρ s . MLP with 17 hidden neurons is proposed as a solution. Then, two MLPs trained with target patterns with ρ s varying in [0,0.5] and [0.5,1], respectively, are combined. This scheme outperforms the single MLP and allows to determine a solution of compromise between complexity and approximation error. A detector composed of MLPs with 17 and 8 hidden units each one is proposed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Van Trees, H.L.: Detection, estimation, and modulation theory, vol. 1. Wiley, Chichester (1968)
Aref, M.R., Nayebi, M.M.: Likelihood-ratio detection. In: IEEE Int. Symp. on Information Theory, Trondheim, Norway, p. 260 (1994)
Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W.: The multilayer perceptron as an aproximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks 1(4), 296–298 (1990)
Wan, E.A.: Neural network classification: a bayesian interpretation. IEEE Transactions on Neural Networks 1(4), 303–305 (1990)
Richard, M.D., Lippmann, R.P.: Neural network classifiers estimate Bayessian a posteriori probabilities. Neural Computation 3, 461–483 (1991)
Jarabo-Amores, P., Rosa-Zurera, M., Gil-Pita, R., López-Ferreras, F.: Suficient Condition for an Adaptive System to Approximate the Neyman-Pearson Detector. In: Proc. IEEE Workshop on Statistical Signal Processing, Bordeaux, France (2005)
Gandhi, P.P., Ramamurti, V.: Neural networks for signal detection in non-gaussian noise. IEEE Transactions on Signal Processing 45(11), 2846–2851 (1997)
Andina, D., Sanz-Gonzlez, J.L.: Comparison of a neural network detector vs. Neyman-Pearson optimal detector. In: Proc. ICASSP 1996, Atlanta, GA, pp. 3573–3576 (1996)
Munro, D.J., Ersoy, O.K., Bell, M.R., Sadowsky, J.S.: Neural network learning of low-probability events. IEEE Transactions on Aerospace and Electronic Systems 32(3), 898–910 (1996)
Mata-Moya, D., Jarabo-Amores, P., Rosa-Zurera, M., López-Ferreras, F., Vicen- Bueno, R.: Approximating the Neyman-Pearson detector for Swerling I Targets with Low Complexity Neural Networks. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3697, pp. 917–922. Springer, Heidelberg (2005)
Jarabo-Amores, P., Gil-Pita, R., Rosa-Zurera, M., López-Ferreras, F.: MLP and RBFN for detecting white gaussian signals in white gaussian interference. In: IWANN 2003. LNCS, vol. 2687, pp. 790–797. Springer, Heidelberg (2003)
El-Jaroudi, A.A., Makhoul, J.: A new error criterion for posterior probability estimation with neural nets. In: Proc. of the Int. Conf. on Neural Networks IJCNN, U.S.A., June 1990, pp. 185–192 (1990)
Nguyen, D., Widrow, B.: Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: Proc. of the Int. Joint Conf. on Neural Networks, pp. 21–26 (1990)
Sanz-Gonzalez, J.L., Andina, D.: Performance analysis of neural network detectors by importance sampling techniques. In: Neural Proc. Letters, vol. 9, pp. 257–269 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de la Mata-Moya, D., Jarabo-Amores, P., Vicen-Bueno, R., Rosa-Zurera, M., López-Ferreras, F. (2006). Neural Network Detectors for Composite Hypothesis Tests. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_36
Download citation
DOI: https://doi.org/10.1007/11875581_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)