.png){kind=icon}
返回
RSS-Based Indoor Positioning Based on Multi-Dimensional Kernel Modeling and Weighted Average Tracking
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2016.2524537.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we discuss a similarity inconsistency phenomenon where the radio signal strength (RSS) signatures of two neighboring positions are dissimilar due to the RSS variation. While matching an observed RSS throughout the radio map, the phenomenon would lead to a jagged similarity distribution. This may break the similarity assumption of the previous works. To address the problem, we proposed a multi-dimensional kernel density estimation (MDKDE) method. By introducing the spatial kernel, the method could adopt neighboring information to enrich the fingerprint. The model can also help to generate a smooth and consistent similarity distribution. Moreover, we formulated the searching of the target location over the continuous domain as an optimization problem. Instead of estimating the optimal location numerically, we also came up with an efficient tracking method, weighted average tracker (WAT). Upon the MDKDE model, WAT can track the target in a simple weighted average method. The experimental results have demonstrated that the proposed system could well model the RSS variation and provide robust positioning performance in an efficient manner.
Keyword:
Indoor localization
multi-dimensional kernel density estimation
radio fingerprint
multi-modal distribution
weighted average tracker
similarity inconsistency
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
IF:
4.5
论文数:
2.1W
被引数:
7.3W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏
查看更多内容