An efficient hierarchical model for multi-source information fusion

作者：

Highlights：

• Key features of IPF and HMM are combined within a single unified framework.

• A hierarchical model (HM) for multi-source information fusion is presented.

• New algorithms are designed for efficiently calibrating the HM.

• HM is relatively robust with respect to sampling rate variability.

• HM presents a lot of flexibility in terms of data availability.

摘要

•Key features of IPF and HMM are combined within a single unified framework.•A hierarchical model (HM) for multi-source information fusion is presented.•New algorithms are designed for efficiently calibrating the HM.•HM is relatively robust with respect to sampling rate variability.•HM presents a lot of flexibility in terms of data availability.

论文关键词：Iterative Proportional Fitting (IPF),Hidden Markov Model (HMM),Hierarchical model (HM),Multi-source information fusion

论文评审过程：Received 26 December 2017, Revised 8 May 2018, Accepted 7 June 2018, Available online 15 June 2018, Version of Record 18 June 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.018