26 March 1998 Doppler frequency estimation with wavelets and neural networks
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In this paper we apply the continuous wavelet transform, along with multilayer feedforward neural networks, to the estimation of time-dependent radar doppler frequency. The wavelet transform employs the real-valued Morlet wavelet, which is well matched to the doppler signals of interest. The neural networks are trained with the Levenberg-Marquardt rule, which is much faster than purely gradient-descent learning algorithms such as back propagation. We also apply Donoho's wavelet denoising with the novel super-Haar wavelet to improve performance for noisy signals. The techniques are applied to the problem of radar proximity fuzing.
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Steven E. Noel, Steven E. Noel, Harold H. Szu, Harold H. Szu, Yogesh J. Gohel, Yogesh J. Gohel, "Doppler frequency estimation with wavelets and neural networks", Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304865; https://doi.org/10.1117/12.304865

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