FFK:富里埃转换模糊-c-意味着卡尔曼过器基于RSSI过机制的室内定位
Chinyang Henry Tseng1, Woei-Jiunn Tsaur2
1Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 23741, Taiwan.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
这项研究引入了一种新的福里埃变换模糊介绍卡尔曼波器 (FFK) 用于在室内定位中接收信号强度指示 (RSSI) 指纹. 通过稳定RSSI值,FFK提高了距离估计的准确性,优于现有的方法.
科学领域:
- 信号处理 信号处理
- 室内定位系统 室内定位系统
- 物联网的物联网,就是物联网.
背景情况:
- 接收信号强度指示 (RSSI) 指纹是常见的室内定位.
- 现有的方法经常使用高斯和卡尔曼过器来进行RSSI指纹.
- RSSI值分布可能是非高斯式,限制了当前波器的性能.
研究的目的:
- 为稳定的RSSI指纹提供一个新的里叶变换模糊介质卡尔曼波器 (FFK).
- 为了提高室内定位系统的距离估计精度.
- 为了解决传统过方法中任意RSSI分布的局限性.
主要方法:
- 开发了FFK机制,集成富里埃变换,模糊C-Means (FCM) 和卡尔曼波器.
- 利用里叶变换从低频域中提取稳定的RSSI值.
- 采用FCM来识别任意RSSI分布中的视线 (LOS) 集群,然后使用卡尔曼过来进行稳定价值估计.
主要成果:
- 与高斯过器,卡尔曼过器及其组合相比,FFK显示出更高的距离估计精度.
- 拟议的FFK方法有效地处理任意的RSSI分配.
- 在现实环境中的实验结果验证了FFK的增强精度.
结论:
- FFK过机制提供了一个更稳定的RSSI指纹,用于准确的室内定位.
- 在基于RSSI的距离估计中,FFK比传统方法提供了显著的改进.
- 这种方法提高了物联网中的室内定位应用程序的可靠性.
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