@inproceedings{6cf35dcc21b24a678fb22de8b46cf75a,
title = "Optimal joint detection and estimation in linear models",
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.",
author = "Jianshu Chen and Yue Zhao and Andrea Goldsmith and Poor, {H. Vincent}",
year = "2013",
doi = "10.1109/CDC.2013.6760569",
language = "English (US)",
isbn = "9781467357173",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4416--4421",
booktitle = "2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013",
address = "United States",
note = "52nd IEEE Conference on Decision and Control, CDC 2013 ; Conference date: 10-12-2013 Through 13-12-2013",
}