PDR/fingerprinting fusion indoor location tracking using RSS recovery and clustering
- Authors
- Koo, Bonhyun; Lee, Sangwoo; Lee, Myungsu; Lee, Dongkeon; Lee, Sangsun; Kim, Sunwoo
- Issue Date
- Oct-2014
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- clustering; fingerprinting; IMU; RSS; WiFi network
- Citation
- IPIN 2014 - 2014 International Conference on Indoor Positioning and Indoor Navigation, pp.699 - 704
- Indexed
- SCOPUS
- Journal Title
- IPIN 2014 - 2014 International Conference on Indoor Positioning and Indoor Navigation
- Start Page
- 699
- End Page
- 704
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/159058
- DOI
- 10.1109/IPIN.2014.7275546
- ISSN
- 0000-0000
- Abstract
- Due to the received signal strength (RSS) variation, WiFi indoor positioning techniques using RSS have difficulties to provide good location estimates. To mitigate the effect of the RSS variation, this paper presents a Kalman filter-based positioning algorithm that is combined with pedestrian dead reckoning and RSS-based fingerprinting positioning. The RSS recovery and clustering methods are also introduced to enhance the accuracy of the fingerprinting positioning. Unlike other existing algorithms, the proposed algorithm estimates biases accumulated in RSS measurements based on the recursive least square estimation and removes them from the measurements. Reference points are effectively selected with clustering using the recovered RSS measurements. Hence, a more accurate location estimate can be obtained in the existence of the RSS variation. The proposed algorithm is implemented into an Android-based smartphone for test.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/159058)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.