Optimal joint detection and estimation in linear models

Jianshu Chen, Yue Zhao, Andrea Goldsmith, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The problem of optimal joint detection and estimation in linear models with Gaussian noise is studied. A simple closed-form expression for the joint posterior distribution of the (multiple) hypotheses and the states is derived. The expression crystalizes the dependence of the optimal detector on the state estimates. The joint posterior distribution characterizes the beliefs ("soft information") about the hypotheses and the values of the states. Furthermore, it is a sufficient statistic for jointly detecting multiple hypotheses and estimating the states. The developed expressions give us a unified framework for joint detection and estimation under all performance criteria.

Original languageEnglish (US)
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4416-4421
Number of pages6
ISBN (Print)9781467357173
DOIs
StatePublished - Jan 1 2013
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: Dec 10 2013Dec 13 2013

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other52nd IEEE Conference on Decision and Control, CDC 2013
CountryItaly
CityFlorence
Period12/10/1312/13/13

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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    Chen, J., Zhao, Y., Goldsmith, A., & Poor, H. V. (2013). Optimal joint detection and estimation in linear models. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013 (pp. 4416-4421). [6760569] (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2013.6760569