Data-driven design and complexity control of time-frequency detectors

In this paper. we introduce a method of designing optimal time-frequency de tectors from training samples, which is potentially of great benefit when f ew a priori information on the nonstationary signal to be detected is avail able. However, achieving good performance with data-driven detectors requir es matching their complexity to the available amount of training samples: r eceivers with a too large number of adjustable parameters often exhibit a p oor generalization performance whereas those with an insufficient complexit y cannot learn all the information available in the design set. Then, using the principle of structural risk minimization proposed by Vapnik, we intro duce procedures which provide powerful tools for tuning the complexity of g eneralized linear detectors and improving their performance. Next, these me thods are successfully experimented on simulated and real data, with linear detectors operating in the time-frequency domain: it is in such high-dimen sional feature spaces thar procedures of deriving reduced-bias receivers fr om training samples are of prime necessity. Finally, we show that our metho dology may offer a helpful support for designing detectors in many applicat ions of current interest, such as biomedical engineering and complex system s monitoring. (C) 1999 Elsevier Science B.V. All rights reserved.