TY - GEN
T1 - Optimal multi-model detection with application to Gaussian problems
AU - He, Yan
AU - Ding, Yingying
AU - Rong Li, X.
N1 - Publisher Copyright:
© 2017 International Society of Information Fusion (ISIF).
PY - 2017/8/11
Y1 - 2017/8/11
N2 - Detection with multiple distributions is considered. Rather than formulating the problem with multiple hypotheses, we formulate the problem in a binary hypothesis testing framework by a multiple model approach. Three classes of the Multi-Model Detection (MMD) problems are considered: simplex, compound, and mixture. Three concepts of optimality are given for these three problems, including Uniformly Most Powerful over Mixtures (UMPM) for the mixture case. The relationships between different optimality are analyzed. A method of designing a UMPM test based on Uniformly Most Powerful (UMP) test is proposed. Several examples of the UMPM test for MMD problems with Gaussian distributions are given. Simulation results are provided that verify the theoretical conclusions.
AB - Detection with multiple distributions is considered. Rather than formulating the problem with multiple hypotheses, we formulate the problem in a binary hypothesis testing framework by a multiple model approach. Three classes of the Multi-Model Detection (MMD) problems are considered: simplex, compound, and mixture. Three concepts of optimality are given for these three problems, including Uniformly Most Powerful over Mixtures (UMPM) for the mixture case. The relationships between different optimality are analyzed. A method of designing a UMPM test based on Uniformly Most Powerful (UMP) test is proposed. Several examples of the UMPM test for MMD problems with Gaussian distributions are given. Simulation results are provided that verify the theoretical conclusions.
KW - Neyman-Pearson criterion
KW - multi-model
KW - statistical detection
KW - uniformly most powerful
UR - https://www.scopus.com/pages/publications/85029422028
U2 - 10.23919/ICIF.2017.8009837
DO - 10.23919/ICIF.2017.8009837
M3 - 会议稿件
AN - SCOPUS:85029422028
T3 - 20th International Conference on Information Fusion, Fusion 2017 - Proceedings
BT - 20th International Conference on Information Fusion, Fusion 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th International Conference on Information Fusion, Fusion 2017
Y2 - 10 July 2017 through 13 July 2017
ER -