SONN and MLP based solutions for detecting fluctuating targets with unknown doppler shift in gaussian interference

Authors:
David Mata-Moya;Pilar Jarabo-Amores;Nerea del-Rey-Maestre;Jose Luis Bárcena-Humanes;Jaime Martín-de-Nicolás
Affiliations:
Signal Theory and Communications Department, Superior Polytechnic School, University of Alcala, Alcala de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcala, Alcala de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcala, Alcala de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcala, Alcala de Henares, Madrid, Spain;Signal Theory and Communications Department, Superior Polytechnic School, University of Alcala, Alcala de Henares, Madrid, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

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Abstract

SONN and MLP based detection schemes are designed for approximating the Neyman-Pearson, NP, detector for detecting fluctuating targets with unknown Doppler shift in Gaussian interference. The optimum NP detector conveys a complex integral, so sub-optimum approaches based on the Constrained Generalized Likelihood Ratio, CGLR, are proposed as reference solutions. Detectors based on a single MLP, a single SONN, and mixtures of them are studied, and their detection capabilities and computational costs evaluated. Results show that the detector based on a mixture of SONNs is able to approximate the CGLR, outperforming the other proposed solutions, with lower computational cost.