26 March 1998 Doppler frequency estimation with wavelets and neural networks
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Abstract
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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