Open AccessGaussian process inference approximation for indoor pedestrian localisation
Author(s) -
Medvesek J.,
Symington A.,
Trost A.,
Hailes S.
Publication year - 2015
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.2014.4436
Subject(s) - clutter , inference , computer science , interpolation (computer graphics) , gaussian process , algorithm , gaussian , position (finance) , artificial intelligence , radar , computer vision , motion (physics) , telecommunications , physics , quantum mechanics , finance , economics
Clutter has a complex effect on radio propagation, and limits the effectiveness of deterministic methods in wireless indoor positioning. In contrast, a Gaussian process ( G P ) can be used to learn the spatially correlated measurement error directly from training samples, and build a representation from which a position can be inferred. A method of exploiting ℬ inference to obtain measurement predictions from within a pose graph optimisation framework is presented. However,G Pinference has a run‐time complexity of ( N 3 ) in the number of training samples N , which precludes it from being called in each optimiser iteration. The novel contributions of this work are a method for building an approximateG Pinference map and an (1) bi‐cubic interpolation strategy for sampling this map during optimisation. Using inertial, magnetic, signal strength and time‐of‐flight measurements between four anchors and a single mobile sensor, it is shown empirically that the presented approach leads to decimetre precision indoor pedestrian localisation.