.png){kind=icon}
返回
Robust route inference and representation for uncertain sensor data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2015.11.023.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Robust particle filter
Personal assistant
Spatial-temporal context-aware services
Uncertain sensor data
Dynamic Bayesian network model
Route model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
EFM: evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏