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Ship target recognition using low resolution radar and neural networks

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2 Author(s)
Inggs, M.R. ; Cape Town Univ., Rondebosch, South Africa ; Robinson, A.D.

The classification of ship targets using low resolution down-range radar profiles together with preprocessing and neural networks is investigated. An implementation of the Fourier-modified discrete Mellin transform is used as a means for extracting features which are insensitive to the aspect angle of the radar. Kohonen's self-organizing map with learning vector quantization (LVQ) is used for the classification of these feature vectors. The use of a feedforward network trained with the backpropagation algorithm is also investigated. The classification system is applied to both simulated and real data sets. Classification accuracies of up to 90% are reported for the real data, provided target aspect angle information is available to within an error not exceeding 30 deg

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Aerospace and Electronic Systems, IEEE Transactions on  (Volume:35 ,  Issue: 2 )