TY - GEN
T1 - Sequential Monte Carlo filtering for location estimation in indoor wireless environments
AU - Ryoo, Jihoon
AU - Choi, Hyunjun
AU - Kim, Hwangnam
PY - 2010
Y1 - 2010
N2 - In this paper, we propose a distributed, infrastructure-free algorithm for supporting self-localization and location-tracking of portable devices in home networks that do not rely on any positioning infrastructure, such as GPS (Global Positioning System). The proposed algorithm employs the received signal strength (RSS) to estimate the current position of each portable device and then elaborates the position with the box-based sequential Monte Carlo (BSMC) method. Simulation results indicate that the proposed algorithm is superior to the well-received Centroid algorithm [1] in terms of the distance estimation error.
AB - In this paper, we propose a distributed, infrastructure-free algorithm for supporting self-localization and location-tracking of portable devices in home networks that do not rely on any positioning infrastructure, such as GPS (Global Positioning System). The proposed algorithm employs the received signal strength (RSS) to estimate the current position of each portable device and then elaborates the position with the box-based sequential Monte Carlo (BSMC) method. Simulation results indicate that the proposed algorithm is superior to the well-received Centroid algorithm [1] in terms of the distance estimation error.
KW - Sequential Monte Carlo method
KW - WiFi radio signal strength based localization
UR - http://www.scopus.com/inward/record.url?scp=77951261747&partnerID=8YFLogxK
U2 - 10.1109/CCNC.2010.5421650
DO - 10.1109/CCNC.2010.5421650
M3 - Conference contribution
AN - SCOPUS:77951261747
SN - 9781424451760
T3 - 2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010
BT - 2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010
T2 - 2010 7th IEEE Consumer Communications and Networking Conference, CCNC 2010
Y2 - 9 January 2010 through 12 January 2010
ER -