Share Email Print

Proceedings Paper

Doppler frequency estimation with wavelets and neural networks
Author(s): Steven E. Noel; Harold H. Szu; Yogesh J. Gohel
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 26 March 1998
PDF: 8 pages
Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304865
Show Author Affiliations
Steven E. Noel, Naval Surface Warfare Ctr. (United States)
Harold H. Szu, Naval Surface Warfare Ctr. (United States)
Yogesh J. Gohel, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 3391:
Wavelet Applications V
Harold H. Szu, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?