A 3-D Indoor Positioning System Using Asymmetry Double-Sided Two-Way Ranging And Chan Assisted Extended Kalman Filter

There are several methods which can be used to locate an object or people in an indoor location. Ultra-wideband (UWB) is a specifically promising indoor positioning technology because of its high accuracy, resistance to interference, and better penetration. This study aims to improve the accuracy of the UWB sensor-based indoor positioning system. To achieve that, the proposed system is trained by using the K-means algorithm with an additional average silhouette method. This helps us to define the optimal number of clusters to be used by the K-means algorithm based on the value of the silhouette coefficient. Fuzzy c-means and mean shift algorithms are added for comparison purposes. This paper also introduces the impact of the Kalman filter while using the measured UWB test points as an input for the Kalman filter in order to obtain a better estimation of the position. As a result, the average localization error is reduced by 43.26% (from 16.3442 cm to 9.2745 cm) when combining the K-means algorithm with the Kalman filter in which the Kalman-filtered UWB-measured test points are used as an input for the proposed system.

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Heading Estimation with Real-time Compensation Based on Kalman Filter Algorithm for an Indoor Positioning System

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi5060098 ◽

2016 ◽

Vol 5 (6) ◽

pp. 98 ◽

Cited By ~ 9

Author(s):

Xin Li ◽

Jian Wang ◽

Chunyan Liu

Keyword(s):

Kalman Filter ◽

Real Time ◽

Indoor Positioning ◽

Positioning System ◽

Kalman Filter Algorithm ◽

Heading Estimation ◽

Indoor Positioning System

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

10.1101/186353 ◽

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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An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01809-y ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Haiyun Yao ◽

Hong Shu ◽

Hongxing Sun ◽

B. G. Mousa ◽

Zhenghang Jiao ◽

...

Keyword(s):

Kalman Filter ◽

Computational Complexity ◽

Unscented Kalman Filter ◽

Indoor Positioning ◽

Positioning System ◽

Positioning Accuracy ◽

Integrity Monitoring ◽

Observation Vector ◽

Indoor Positioning System ◽

Monitoring Algorithm

AbstractIndoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.

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