约束多模型最大后验估计采用list Viterbi算法

2017 20th International Conference on Information Fusion (Fusion) Pub Date : 2017-07-01 DOI:10.23919/ICIF.2017.8009649

V. Jilkov, Jeffrey H. Ledet, X. R. Li

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引用次数: 1

摘要

本文提出了一种基于期望最大化(EM)方法的约束多模型(MM)最大后验(MAP)估计的新方法，该方法采用了已有的约束序列列表Viterbi算法(CSLVA)。该方法是通用的，适用于任何类型的约束，只要它们是可验证的。设计了具体的实现算法，并通过仿真验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Constrained multiple model maximum a posteriori estimation using list Viterbi algorithm

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.

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来源期刊

2017 20th International Conference on Information Fusion (Fusion)

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