TY - JOUR
T1 - An Indoor Localization Approach Based on Fingerprint and Time-Difference of Arrival Fusion
AU - Yang, Haoyu
AU - Wang, Yuanshuo
AU - Li, Dongchen
AU - Li, Tiancheng
N1 - Publisher Copyright:
© 2022 Beijing Institute of Technology. All rights reserved.
PY - 2022/12
Y1 - 2022/12
N2 - In this paper, an effective target locating approach based on the fingerprint fusion positioning (FFP) method is proposed which integrates the time-difference of arrival (TDOA) and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios. The FFP method fuses the pedestrian dead reckoning (PDR) estimation to solve the moving target localization problem. We also introduce auxiliary parameters to estimate the target motion state. Subsequently, we can locate the static pedestrians and track the the moving target. For the case study, eight access stationary points are placed on a bookshelf and hypermarket; one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf. We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor, weighted k-nearest neighbor, pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter, unscented Kalman filter and particle filter (PF). The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors, especially in the 3D scenarios. Simulation results corroborate the effectiveness of our proposed approach.
AB - In this paper, an effective target locating approach based on the fingerprint fusion positioning (FFP) method is proposed which integrates the time-difference of arrival (TDOA) and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios. The FFP method fuses the pedestrian dead reckoning (PDR) estimation to solve the moving target localization problem. We also introduce auxiliary parameters to estimate the target motion state. Subsequently, we can locate the static pedestrians and track the the moving target. For the case study, eight access stationary points are placed on a bookshelf and hypermarket; one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf. We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor, weighted k-nearest neighbor, pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter, unscented Kalman filter and particle filter (PF). The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors, especially in the 3D scenarios. Simulation results corroborate the effectiveness of our proposed approach.
KW - 3D indoor localization
KW - fingerprint fusion positioning
KW - pedestrian dead reckoning
KW - received signal strength
KW - time-difference of arrival
UR - http://www.scopus.com/inward/record.url?scp=85153364949&partnerID=8YFLogxK
U2 - 10.15918/j.jbit1004-0579.2022.060
DO - 10.15918/j.jbit1004-0579.2022.060
M3 - 文章
AN - SCOPUS:85153364949
SN - 1004-0579
VL - 31
SP - 570
EP - 583
JO - Journal of Beijing Institute of Technology (English Edition)
JF - Journal of Beijing Institute of Technology (English Edition)
IS - 6
ER -