Reduced-complexity tracking scheme based on adaptive weighting for location estimation

This study presents an efficient location tracking algorithm to reduce the computational complexity of the conventional Kalman filtering algorithm. In the proposed location estimation and tracking approach, using the inherent message-passing nature of factor graphs, the data information is passed efficiently between the variable nodes and the factor nodes by taking weights based on the message reliability, thus simplifying implementation of the Bayesian filtering approach for location tracking. Numerical simulations and experimental results show that the proposed location tracking scheme not only can achieve the location accuracy close to that of the Kalman filtering scheme, but also has lower computational complexity with decoupling approach.