A Hexagonal Coverage LED-ID Indoor Positioning Based on TDOA with Extended Kalman Filter

2013 ◽  
Author(s):  
Attaphongse Taparugssanagorn ◽  
Siwaruk Siwamogsatham ◽  
Carlos Pomalaza-Raez
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Indoor Positioning

scholarly journals Indoor Positioning Tightly Coupled Wi-Fi FTM Ranging and PDR Based on the Extended Kalman Filter for Smartphones

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 49671-49684 ◽  
Author(s):  
Meng Sun ◽  
Yunjia Wang ◽  
Shenglei Xu ◽  
Hongxia Qi ◽  
Xianxian Hu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Indoor Positioning ◽  
Tightly Coupled

Extended Kalman Filter for wireless LAN based indoor positioning

2008 ◽  
Vol 45 (4) ◽  
pp. 960-971 ◽  
Author(s):  
Jaegeol Yim ◽  
Chansik Park ◽  
Jaehun Joo ◽  
Seunghwan Jeong
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Wireless Lan ◽  
Indoor Positioning

Filtering Methods to Improve the Accuracy of Indoor Positioning Data for Dairy Cows

2017 ◽  
Author(s):  
Matti Pastell ◽  
Lilli Frondelius ◽  
Mikko Järvinen ◽  
Juha Backman
Keyword(s):  
Kalman Filter ◽  
Dairy Cows ◽  
Extended Kalman Filter ◽  
Median Filter ◽  
Measurement Data ◽  
Indoor Positioning ◽  
Positioning Systems ◽  
Animal Presence ◽  
Indoor Positioning System

AbstractSeveral indoor positioning systems for livestock buildings have been developed to be used as a tool in automated animal welfare monitoring. In many environments the measurements from positioning systems still contain unwanted noise and the quality of the measurement data can be enhanced using filters.The aim of this study was to develop an efficient filter for positioning data measured from dairy cows with UWB-based indoor positioning system in a free stall barn. We developed and tested a heuristic jump filter combined with median filter and extended Kalman filter. The performance of the filters were compared against reference data collected from Insentec Roughage intake feeders and scan sampling of animal presence in a specific lying stall with over 1500 reference observations from both methods.We were able to improve the quality of the positioning data significantly using filtering. The 9th order median filter provided best estimates for cow position when the cows were not moving with median 100% of measurements located in correct stall and 84% in correct feeding trough when compared to the reference observations and measurements. The extended Kalman filter also improved the positioning accuracy significantly when compared to raw data and provides better of estimates of the trajectory of moving cows.

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