DATA-DRIVEN SIGNAL DETECTION AND CLASSIFICATION 


Akbar M. Sayeed 

Department of Electrical and Computer Engineering 
Rice University
6100 S. Main Street 
Houston, TX 77005    

Proceeding of the 1997 IEEE International Conference on 
Acoustics, Speech and Signal Processing - ICASSP '97, 
Munich, Germany 

ABSTRACT:

In many practical detection and classification problems,
the signals of interest exhibit some uncertain nuisance 
parameters, such as the unknown delay and Doppler in radar.
For optimal performance, the form of such parameters must 
be known and exploited as is done in the generalized 
likelihood ratio test (GLRT). In practice, the statistics 
required for designing the GLRT processors are not 
available a priori  and must be estimated from limited 
training data.  Such design is virtually  impossible in 
general due to t wo major difficulties: identifying  the 
appropriate nuisance parameters, and estimating the 
corresponding GLRT statistics. We address this problem 
by using recent results that relate joint signal 
representations (JSRs), such as time-frequency and time-scale 
representations, to quadratic GLRT processors for a wide 
variety of nuisance parameters. We propose a general 
data-driven framework that: 1) identifies the appropriate 
nuisance parameters from an arbitrarily chosen finite set, 
and 2) estimates the second-order statistics that characterize 
the corresponding JSR-based GLRT processors.