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Spectrum estimation by neural networks and their use for target classification by radar

Radar targets with moving components (engines, propellers, etc.) are well characterized in the frequency domain so that the estimation of spectral parameters can be used in the process of extracting features for their classification. With non-imaging scanning radars the application of spectral parameter estimation for target classification is limited by a short time on target and other radar parameters. To alleviate these limitations, artificial neural networks have been used bringing with them the long training time issue. This paper suggests a neural network approach that capitalizes on the prescence of spectral components and reduces the training time. This approach divides the training of the multilayer perceptron (MLP) in two steps: pretraining and final training. Pretraining guides the MLP to recognize the Fourier basis set of signals. The final training for target classification is then facilitated.