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Proceedings Paper

Weighted time-frequency and time-scale transforms for nonstationary signal detection
Author(s): Lora G. Weiss; Leon H. Sibul
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Paper Abstract

Maximum likelihood detectors of narrowband, non-stationary random echos in Gaussian noise can be efficiently implemented in the time-frequency domain. When the transmitted signals have large time-bandwidth products, the natural implementation of estimators and detectors is the time-scale or wavelet transform domain implementation. This paper extends the wavelet transform implementations to include weighted time-frequency or time-scale (TF/TS) transforms. We define weighted TF/TS transforms using Reproducing Kernel Hilbert Space inner products. Inverses of these weighted TF/TS transforms are also given. The particular case of the weight being the inverse noise covariance is presented. We show how weighted transforms are used in the estimator-correlator detection statistic for complex scattering environments in conjunction with cascaded scattering functions so that the resulting detection statistic is much more robust. The weighted TF/TS transform turns out to be a natural transform for solving nonstationary detector, estimation, and filtering problems and has important applications to transient signal estimation in multipath channels with colored non-stationary Gaussian noise.

Paper Details

Date Published: 30 October 1997
PDF: 10 pages
Proc. SPIE 3169, Wavelet Applications in Signal and Image Processing V, (30 October 1997); doi: 10.1117/12.279697
Show Author Affiliations
Lora G. Weiss, The Pennsylvania State Univ. (United States)
Leon H. Sibul, The Pennsylvania State Univ. (United States)

Published in SPIE Proceedings Vol. 3169:
Wavelet Applications in Signal and Image Processing V
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

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