Indoor positioning using WLAN coverage area estimates
Authors:
- 1 September 2010
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-7
Abstract
This paper introduces a novel method for positioning using coverage area estimates of wireless communication nodes. The coverage areas are estimated in a Bayesian inference framework using location fingerprints that are collected in an offline calibration phase, and the estimated coverage areas are stored in a database. In the online positioning phase the coverage areas of the heard communication nodes are used to infer the position of the mobile terminal. Floor plan information is used to enhance the positioning accuracy. In a field study comparing Kalman Filter, Box Filter and Particle Filter using real WLAN measurement data, it is found that Kalman Filter achieves almost the same accuracy as Box Filter and Particle Filter but with smaller computational load.Keywords
This publication has 9 references indexed in Scilit:
- On the RBF-based positioning using WLAN signal strength fingerprints2010
- Positioning with coverage area estimates generated from location fingerprints2010
- Kalman-type positioning filters with floor plan information2008
- A novel backtracking particle filter for pattern matching indoor localization2008
- Efficient Gaussian mixture filter for hybrid positioning2008
- Enhancing the Map Usage for Indoor Location-Aware Systems2007
- Map-aided indoor mobile positioning system using particle filter2005
- RADAR: an in-building RF-based user location and tracking system2002
- Recursive Bayesian estimation using piece-wise constant approximationsAutomatica, 1988