TY - JOUR
T1 - Single-Road-Constrained Positioning Based on Deterministic Trajectory Geometry
AU - Li, Tiancheng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/1
Y1 - 2019/1
N2 - We consider the single-road-constrained estimation problem for positioning a target that moves on a single, deterministic, and exactly known trajectory. Based on the geometry of the trajectory curve, we cast the constrained estimation problem as an unconstrained problem with reduced state dimension. Two approaches are devised based on a Markov transition model for unscented Kalman filtering and a continuous function of time for (weighted) least square fitting, respectively. A popular simulation model has been used for demonstrating the performance of the proposed approaches in comparison with the existing approaches.
AB - We consider the single-road-constrained estimation problem for positioning a target that moves on a single, deterministic, and exactly known trajectory. Based on the geometry of the trajectory curve, we cast the constrained estimation problem as an unconstrained problem with reduced state dimension. Two approaches are devised based on a Markov transition model for unscented Kalman filtering and a continuous function of time for (weighted) least square fitting, respectively. A popular simulation model has been used for demonstrating the performance of the proposed approaches in comparison with the existing approaches.
KW - Bayesian estimation
KW - constrained filtering
KW - least squares fitting
KW - mobile positioning
UR - http://www.scopus.com/inward/record.url?scp=85056209089&partnerID=8YFLogxK
U2 - 10.1109/LCOMM.2018.2879478
DO - 10.1109/LCOMM.2018.2879478
M3 - 文章
AN - SCOPUS:85056209089
SN - 1089-7798
VL - 23
SP - 80
EP - 83
JO - IEEE Communications Letters
JF - IEEE Communications Letters
IS - 1
M1 - 8522027
ER -