TIME-FREQUENCY DETECTORS

Akbar M. Sayeed and Douglas L. Jones

Coordinated Science Laboratory
University of Illinois
Urbana, IL 61801 

Proceedings of the 1996 Conference on Information Sciences and 
Systems - CISS'96, Princeton, NJ.  

Time-frequency representations (TFRs) provide a powerful and 
flexible structure for designing optimal detectors in a variety of 
nonstationary scenarios.  In this paper, we describe a TFR-based 
framework for optimal detection of arbitrary second-order stochastic 
signals, with certain unknown or random nuisance parameters, in the 
presence of Gaussian noise. The framework provides a useful model 
for many important applications including machine fault diagnostics 
and radar/sonar. We emphasize a subspace-based formulation of such 
TFR detectors which can be exploited in a variety of ways to design 
new techniques. In particular, we explore an extension based on 
multi-channel/sensor measurements that are often available in practice 
to facilitate improved signal processing.  In addition to potentially 
improved performance, the subspace-based interpretation of such 
multi-channel detectors provides useful information about the 
physical mechanisms underlying the signals of interest.