TY - JOUR
T1 - Distributed Hybrid Particle/FIR Filtering for Mitigating NLOS Effects in TOA-Based Localization Using Wireless Sensor Networks
AU - Pak, Jung Min
AU - Ahn, Choon Ki
AU - Shi, Peng
AU - Shmaliy, Yuriy S.
AU - Lim, Myo Taeg
N1 - Funding Information:
Manuscript received March 30, 2016; revised June 15, 2016; accepted July 15, 2016. Date of publication September 13, 2016; date of current version May 10, 2017. This work was supported in part by the Brain Korea 21 Plus Project in 2016, in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning under Grant NRF-2014R1A1A1006101, in part by the Basic Science Research Program through the NRF funded by the Ministry of Education under Grant NRF-2016R1D1A1B01016071, in part by the National Natural Science Foundation of China under Grant 61573112 and Grant U1509217, and in part by the Australian Research Council under Grant DP140102180 and Grant LP140100471. (Corresponding author: Choon Ki Ahn.) J. M. Pak is with the Department of Electrical Engineering, Wonkwang University, Iksan 54538, South Korea (e-mail: destin11@wku.ac.kr).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2017/6
Y1 - 2017/6
N2 - For indoor localization based on wireless sensor networks, the transmission of wireless signals can be disrupted by obstacles and walls. This situation, called non-line-of-sight (NLOS), degrades localization accuracy and may lead to localization failures. This paper proposes a new NLOS identification algorithm based on distributed filtering to mitigate NLOS effects, including localization failures. Rather than processing all measurements via a single filter, the proposed algorithm distributes the measurements among several local filters. Using distributed filtering and data association techniques, abnormal measurements due to NLOS are identified, and negative effects can be prevented. To address cases of localization failures due to NLOS, the hybrid particle finite impulse response filter (HPFF) was adopted. The resulting distributed HPFF can self-recover by detecting failures and resetting the algorithm. Extensive simulations of indoor localization using time of arrival measurements were performed for various NLOS situations to demonstrate the effectiveness of the proposed algorithm.
AB - For indoor localization based on wireless sensor networks, the transmission of wireless signals can be disrupted by obstacles and walls. This situation, called non-line-of-sight (NLOS), degrades localization accuracy and may lead to localization failures. This paper proposes a new NLOS identification algorithm based on distributed filtering to mitigate NLOS effects, including localization failures. Rather than processing all measurements via a single filter, the proposed algorithm distributes the measurements among several local filters. Using distributed filtering and data association techniques, abnormal measurements due to NLOS are identified, and negative effects can be prevented. To address cases of localization failures due to NLOS, the hybrid particle finite impulse response filter (HPFF) was adopted. The resulting distributed HPFF can self-recover by detecting failures and resetting the algorithm. Extensive simulations of indoor localization using time of arrival measurements were performed for various NLOS situations to demonstrate the effectiveness of the proposed algorithm.
KW - Distributed filtering
KW - distributed hybrid particle/finite impulse response (FIR) filter
KW - indoor localization
KW - non-line-of-sight (NLOS)
KW - wireless sensor network (WSN)
UR - http://www.scopus.com/inward/record.url?scp=85010897635&partnerID=8YFLogxK
U2 - 10.1109/TIE.2016.2608897
DO - 10.1109/TIE.2016.2608897
M3 - Article
AN - SCOPUS:85010897635
SN - 0278-0046
VL - 64
SP - 5182
EP - 5191
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
IS - 6
M1 - 7565735
ER -