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Constrained multiple model maximum a posteriori estimation using list Viterbi algorithm

  • University of New Orleans

科研成果: 书/报告/会议事项章节会议稿件同行评审

1 引用 (Scopus)

摘要

This paper proposes a new approach for constrained multiple model (MM) maximum a posteriori (MAP) estimation through the expectation-maximization (EM) method by using our previously developed constrained sequential list Viterbi algorithm (CSLVA). The approach is general and applicable for any type of constraints provided they are verifiable. Specific algorithms for implementation are designed, and the performance of the proposed method is illustrated by simulation.

源语言英语
主期刊名20th International Conference on Information Fusion, Fusion 2017 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9780996452700
DOI
出版状态已出版 - 11 8月 2017
已对外发布
活动20th International Conference on Information Fusion, Fusion 2017 - Xi'an, 中国
期限: 10 7月 201713 7月 2017

出版系列

姓名20th International Conference on Information Fusion, Fusion 2017 - Proceedings

会议

会议20th International Conference on Information Fusion, Fusion 2017
国家/地区中国
Xi'an
时期10/07/1713/07/17

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