UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Gaussian process inference approximation for indoor pedestrian localisation

Medvesek, J; Symington, A; Trost, A; Hailes, S; (2015) Gaussian process inference approximation for indoor pedestrian localisation. Electronics Letters , 51 (5) pp. 417-419. 10.1049/el.2014.4436. Green open access

[thumbnail of ELL-2014-4436.pdf]
Preview
Text
ELL-2014-4436.pdf
Available under License : See the attached licence file.

Download (360kB)

Abstract

Clutter has a complex effect on radio propagation, and limits the effect- iveness of deterministic methods in wireless indoor positioning. In contrast, a Gaussian process ( GP ) 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 GP inference to obtain measurement predictions from within a pose graph optimisation framework is presented. However, GP inference has a run-time complexity of O ( N 3 ) in the number of train- ing samples N , which precludes it from being called in each optimiser iteration. The novel contributions of this work are a method for building an approximate GP inference map and an O (1) bi-cubic interpolation strategy for sampling this map during optimisation. Using inertial, magnetic, signal strength and time-of- fl ight measurements between four anchors and a single mobile sensor, it is shown empirically that the presented approach leads to decimetre precision indoor pedestrian localisation

Type: Article
Title: Gaussian process inference approximation for indoor pedestrian localisation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1049/el.2014.4436
Publisher version: http://dx.doi.org/10.1049/el.2014.4436
Language: English
Additional information: This paper is a postprint of a paper submitted to and accepted for publication in Electronics Letters and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library
Keywords: Gaussian processes, approximation theory, interpolation, clutter, indoor radio, pedestrians, radionavigation, graph theory, computational complexity, optimisation, iterative methods, Gaussian process inference approximation, indoor pedestrian localisation, clutter, radio propagation, deterministic methods, wireless indoor positioning, spatially correlated measurement error, training samples, GP inference method, pose graph optimisation framework, run-time complexity, optimiser iteration, O(1) bi-cubic interpolation strategy, signal strength, time-of-flight measurements, magnetic strength, inertial strength, single mobile sensor, decimetre precision indoor pedestrian localisation
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1467275
Downloads since deposit
141Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item