Accuracy of Bluetooth based Indoor Positioning using different Pattern Recognition Techniques

Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications.

[1]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .

[2]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[3]  G. Celeux,et al.  Regularized Gaussian Discriminant Analysis through Eigenvalue Decomposition , 1996 .

[4]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Adrian E. Raftery,et al.  MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering , 2006 .

[8]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[9]  Klaithem Al Nuaimi,et al.  A survey of indoor positioning systems and algorithms , 2011, 2011 International Conference on Innovations in Information Technology.

[10]  Andy Hopper,et al.  The Anatomy of a Context-Aware Application , 1999, Wirel. Networks.

[11]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[12]  Paola Zuccolotto,et al.  Variable Selection Using Random Forests , 2006 .

[13]  Luca Mainetti,et al.  A survey on indoor positioning systems , 2014, 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[14]  Yunhao Liu,et al.  LANDMARC: Indoor Location Sensing Using Active RFID , 2004, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[15]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[16]  Andy Hopper,et al.  Active badges and personal interactive computing objects , 1992 .

[17]  A. Raftery,et al.  Model-based Gaussian and non-Gaussian clustering , 1993 .

[18]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[19]  Xiao Liu,et al.  A Comprehensive Study of Bluetooth Fingerprinting-Based Algorithms for Localization , 2013, 2013 27th International Conference on Advanced Information Networking and Applications Workshops.

[20]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.