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Proceedings Paper

Applications of time-frequency and time-scale transforms to ultra-wideband radar transient signal detection
Author(s): Monique P. Fargues; William A. Brooks
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Paper Abstract

In this paper we use a non-stationary approach and analyze ultra-wideband (UWB) radar data using time-frequency and time-scale transformations. The time-frequency transformations considered are the Short-Time Fourier Transform (STFT), the Wigner-Ville Distribution (WD), the Instantaneous Power Spectrum (IPS), and the ZAM transform. Two discrete implementations of the Wavelet Transform (DWT) are also investigated: the decimated A- trous algorithm proposed by Holschneider et al, which uses non-orthogonal wavelets; and the Mallat algorithm, which employs orthogonal wavelets. The transients under study are UWB radar returns from a boat (with and without corner reflector) in the presence of sea clutter, multipath, and radio frequency interferences (RFI). Results show that all time-frequency and time-scale transforms clearly detect the transient radar returns corresponding to the boat with a corner reflector. However, as the radar cross section of the target decreases (boat without a corner reflector), results change drastically as the RFI component dominates the signal. Simulations show that the Instantaneous Power Spectrum may be better adapted for localizing the transient among the time-frequency techniques studied. The decimated A-trous algorithm has the best time resolution of the techniques studied as the return appears better localized in the scalogram.

Paper Details

Date Published: 1 November 1993
PDF: 14 pages
Proc. SPIE 2027, Advanced Signal Processing Algorithms, Architectures, and Implementations IV, (1 November 1993); doi: 10.1117/12.160433
Show Author Affiliations
Monique P. Fargues, Naval Postgraduate School (United States)
William A. Brooks, Naval Postgraduate School (United States)


Published in SPIE Proceedings Vol. 2027:
Advanced Signal Processing Algorithms, Architectures, and Implementations IV
Franklin T. Luk, Editor(s)

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