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Mathematical Problems in Engineering
Volume 2015, Article ID 397298, 32 pages
Review Article

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

1School of Computer Science & Technology, Huazhong University of Science, Wuhan 430074, China
2Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China
3National Engineering Research Center for Geographic Information System, Wuhan 430074, China
4Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA

Received 21 January 2015; Accepted 26 March 2015

Academic Editor: Paolo Maria Mariano

Copyright © 2015 Jianga Shang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.