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.
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Mata-Moya, D., Jarabo-Amores, P., del-Rey-Maestre, N., Bárcena-Humanes, J.L., Martín-de-Nicolás, J. (2013). SONN and MLP Based Solutions for Detecting Fluctuating Targets with Unknown Doppler Shift in Gaussian Interference. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_59
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DOI: https://doi.org/10.1007/978-3-642-38679-4_59
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