Open AccessOne time‐step particle smoothing for radio range‐based indoor position tracking
Author(s) -
Yang Yuan,
Wu Huaming,
Dai Peng,
Zhang Bo
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.3291
Subject(s) - smoothing , ranging , tracking (education) , computer science , position (finance) , particle filter , computation , algorithm , context (archaeology) , range (aeronautics) , sequential estimation , bayesian probability , real time computing , kalman filter , computer vision , artificial intelligence , engineering , telecommunications , psychology , pedagogy , paleontology , finance , economics , biology , aerospace engineering
In the context of sequential estimation of radio range‐based indoor position tracking, Bayesian smoothing framework is promising as involving past, present and future observations. The performance and practicability of a smoothing method greatly depend on how many and how future observations are incorporated. Aiming at real‐time locating systems, the authors propose one time‐step smoothing form on sequential Monte Carlo methods, including four popular Bayesian smoothers and a novel one time‐step smoothed filtering (SF) algorithm. The smoothing algorithms are evaluated through two‐dimensional position tracking on a real‐world indoor test‐bed. The authors present results that the proposed SF improves tracking performance requiring very limited computation and memory, which is applicable for real‐time indoor position tracking. Moreover, the one time‐step smoothing form is validated to mitigate ranging errors and smooth positioning trajectories.