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.
Ship target recognition using low resolution radar and neural networks
IEEE Transactions on Aerospace and Electronic Systems ; 35 , 2 ; 386-393
1999-04-01
404086 byte
Article (Journal)
Electronic Resource
English
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