Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
A wireless LAN-based indoor positioning technology
IBM Journal of Research and Development
COMPASS: A probabilistic indoor positioning system based on 802.11 and digital compasses
WiNTECH '06 Proceedings of the 1st international workshop on Wireless network testbeds, experimental evaluation & characterization
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A survey of indoor positioning systems for wireless personal networks
IEEE Communications Surveys & Tutorials
Survey of Wireless Indoor Positioning Techniques and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On the comparability of indoor localization systems' accuracy
Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
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Aiming to solve one of the serious problems of radio-signal strength (RSS) indoor positioning (namely, fluctuation of RSS values due to reflective waves), a novel indoor-positioning method based on RSS distribution modeling (called "RSS distribution modeling using the mirror-image method," RD-MMI), was developed and evaluated. With RDMMI, a model of RSS distribution is created by using measured RSS and considering reflective waves. RDMMI achieves an average positioning error of 4 m, which is better than that of conventional nearest-neighbor and trilateration methods by 50 to 60%. Furthermore, it accomplishes average positioning error of 5.4 m even with a small number of non-uniformly placed RSS measurements.