ASA 130th Meeting - St. Louis, MO - 1995 Nov 27 .. Dec 01

4pSP3. Application of wavelet transform to fluctuation based processing.

Jacob George

Ronald A. Wagstaff

Naval Res. Lab., Code 7176, Stennis Space Center, MS 39529

Recently the topic of wavelet analysis has received considerable attention from physicists, engineers, and mathematicians [C. K. Chui, An Introduction to Wavelets (Academic, New York, 1992)]. One well-known feature of a wavelet transform is the choice of a flexible time window which automatically narrows when observing high-frequency phenomena and widens when studying low-frequency environments, in contrast to a fixed window in traditional Fourier transforms. Alternately, the wavelet transform can be viewed as a two-parameter representation of a signal. These features have been used with advantage in time-frequency analyses [Badiey et al., ``Shallow water acoustic/ geoacoustic experiments at the New Jersey Atlantic Generating Station site,'' J. Acoust. Soc. Am. 96, 3593--3604 (1994); Drumheller et al., ``Identification and synthesis of acoustic scattering components via the wavelet transform,'' J. Acoust. Soc. Am. 97, 3649--3656 (1995)]. Fluctuation based processing which has recently been developed at NRL, takes advantage of the time fluctuations of the signal to improve the signal/noise ratio and to enhance signal detection for several frequencies. The improvements in such processing due to the use of wavelet representation will be discussed. [Work supported by ONR.]