Optimal quadratic detection and estimation using generalized joint signal representations

Akbar M. Sayeed, Douglas L. Jones

Research output: Contribution to journalArticlepeer-review


Time-frequency analysis has recently undergone significant advances in two main directions: statistically optimized methods that extend the scope of time-frequency-based techniques from merely exploratory data analysis to more quantitative application and generalized joint signal representations that extend time-frequency-based methods to a richer class of nonstationary signals. This paper fuses the two advances by developing optimal detection and estimation techniques based on generalized joint signal representations. By generalizing the statistical methods developed for time-frequency representations to arbitrary joint signal representations this paper develops a unified theory applicable to a wide variety of problems in nonstationary statistical signal processing.

Original languageEnglish (US)
Pages (from-to)3031-3043
Number of pages13
JournalIEEE Transactions on Signal Processing
Issue number12
StatePublished - 1996
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Optimal quadratic detection and estimation using generalized joint signal representations'. Together they form a unique fingerprint.

Cite this