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Neural Networks for Sequential Discrimination of Radar Targets

dc.contributor.authorHaimerl, Joseph A.en_US
dc.contributor.authorGeraniotis, Evaggelos A.en_US
dc.description.abstractIn this paper, perceptron neural networks are applied to the problem of discriminating between two classes of radar returns. The perceptron neural networks are used as nonlinearities in two threshold sequential discriminators which act upon samples of the radar return. The test statistic compared to the n - K + 1, thresholds is of the form T n (Z) = j = 1 g ( Z j , Z J + 1, ...., Z j + K - 1 ) where, Z i, i = 1, 2, 3, ..... are the radar samples and g () is the nonlinearity formed by the neural network. Numerical results are presented and compared to existing discrimination schemes.en_US
dc.format.extent734013 bytes
dc.relation.ispartofseriesISR; TR 1991-26en_US
dc.subjectneural systemsen_US
dc.subjectrobust information processingen_US
dc.subjectsignal processingen_US
dc.subjectCommunication en_US
dc.subjectSignal Processing Systemsen_US
dc.titleNeural Networks for Sequential Discrimination of Radar Targetsen_US
dc.typeTechnical Reporten_US

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