Robust route inference and representation for uncertain sensor data
- Authors
- Baek, Haejung; Kim, Je-Min; Park, Young-Tack
- Issue Date
- Jan-2016
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Robust particle filter; Personal assistant; Spatial-temporal context-aware services; Uncertain sensor data; Dynamic Bayesian network model; Route model
- Citation
- COMPUTERS & ELECTRICAL ENGINEERING, v.49, pp.236 - 246
- Journal Title
- COMPUTERS & ELECTRICAL ENGINEERING
- Volume
- 49
- Start Page
- 236
- End Page
- 246
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/7725
- DOI
- 10.1016/j.compeleceng.2015.11.023
- ISSN
- 0045-7906
- Abstract
- This paper proposes a robust particle filter to deal with incomplete sensor data to predict the user's routes and represents users' movements using a dynamic Bayesian network model that patterns the user's spatiotemporal routine. The proposed particle filter includes robust particle generation to supplement any incorrect and incomplete sensor information, efficient switching/weight functions to reduce computation complexity while considering uncertainty, and resampling to enhance the accuracy of the particles by solving the degeneracy problem. The robust particle filter enhances the accuracy and efficiency with which a user's routes and destinations are determined. (C) 2015 Elsevier Ltd. All rights reserved.
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Collections - College of Information Technology > School of Computer Science and Engineering > 1. Journal Articles
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