Improvement Schemes for Indoor Mobile Location Estimation: A Survey

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....

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-32
Main Authors: Wang, Di, Gu, Fuqiang, Hu, Xuke, Shang, Jianga, Yu, Shengsheng
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer engineering
Dead reckoning
Error analysis
Extended Kalman filter
Fingerprinting
Fuses
Indoor
Indoor environments
Kalman filters
Localization
Localization method
Markov chains
Matching
Mathematical models
Mathematical problems
Optimization
Performance evaluation
Position (location)
Probabilistic methods
Triangulation
Ubiquitous computing
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Summary: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 onthe 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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/397298