Linear Time-Frequency Representations for Transient Signal Detection and Classification.
Abstract
This dissertation examines the use of linear time frequency representations in the detection and classification of transient signals. In particular, the Gabor transform and short time Fourier transform (STFT) are shown to be effective tools in detecting and classifying signals that are accurately modeled by linear subspaces. Transient signal detection is studied first, within a framework that expresses transient signals as linear combinations of time frequency shifted, one sided exponential window functions. For the case where signal components have known locations in the time-frequency plane, it is shown that a generalized likelihood ratio test (GLRT) detector based on the oversampled Gabor transform is more robust to mismatch than GLRT detectors based on the critically sampled Gabor transform and critically sampled STFT. For the case where signal component locations are not precisely known, it is shown that, for a given transform, a GLRT detector which does not make assumptions about component location information is more robust to component location mismatch than a GLRT detector which does make those assumptions. When the oversampled Gabor transform is used for data reduction, one of its main drawbacks is its lack of stability: small variations in a signal can cause large variations in the magnitudes of the Gabor coefficients. Thus, several modifications designed to improve the stability of the over-sampled Gabor transform has been widely used, and the transform is stable in this form. However, it is shown here that there are several serious problems with the expanded form of the oversampled Gabor transform that make it unsuitable for use in transient signal detection.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1995
- Accession Number
- ADA309178
Entities
People
- Nigel Lee
- Stuart C. Schwartz
Organizations
- Princeton University