scholarly journals One time‐step particle smoothing for radio range‐based indoor position tracking

2020 ◽  
Vol 56 (7) ◽  
pp. 360-362 ◽  
Author(s):  
Yuan Yang ◽  
Huaming Wu ◽  
Peng Dai ◽  
Bo Zhang
Keyword(s):  
Position Tracking ◽  
Time Step ◽  
Radio Range ◽  
Indoor Position Tracking

scholarly journals Discovering Influential Positions in RFID-Based Indoor Tracking Data

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 330
Author(s):  
Ye Jin ◽  
Lizhen Cui
Keyword(s):  
Moving Objects ◽  
Indoor Localization ◽  
Rapid Development ◽  
Shopping Mall ◽  
Position Tracking ◽  
Tracking Data ◽  
H Index ◽  
Indoor Space ◽  
Indoor Tracking ◽  
Indoor Position Tracking

The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

scholarly journals Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation

2015 ◽  
Vol 11 (8) ◽  
pp. 674635 ◽  
Author(s):  
Sangwoo Lee ◽  
Bongkwan Cho ◽  
Bonhyun Koo ◽  
Sanghwan Ryu ◽  
Jaehoon Choi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Position Tracking ◽  
Self Calibration ◽  
Indoor Position Tracking

Indoor Position Tracking Using Multiple Optical Receivers

2016 ◽  
Vol 34 (4) ◽  
pp. 1166-1176 ◽  
Author(s):  
Muhammad Yasir ◽  
Siu-Wai Ho ◽  
Badri N. Vellambi
Keyword(s):  
Position Tracking ◽  
Optical Receivers ◽  
Indoor Position Tracking
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