Indoor localization algorithm based on iterative grid clustering and AP scoring

Indoor localization is of great importance in daily and commercial applications. This paper proposes a novel indoor localization algorithm based on iterative K-means and grid scoring (KS) and a mechanism of access point (AP) scoring. The basic approach of the proposed algorithm is composed of a two-step iteration. The first step is to randomly select a group of APs. Then, the mobile terminal is located into one cluster based on the received signal strength of the selected APs and the score of all the grids belonging to this cluster. After several iterations, the location estimation is selected as the grid with the highest score. To further improve the localization accuracy, AP scoring (AS) is adopted to select the APs with superior localization capability. The suggested algorithm can locate a position effectively with relatively high accuracy. The expected results are demonstrated using simulations.

[1]  Tuo Shen,et al.  Online adaptive positioning algorithm for public location services in indoor places , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[2]  Lin Ma,et al.  WLAN indoor positioning algorithm based on sub-regions information gain theory , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[3]  Armin Wittneben,et al.  Efficient Training Phase for Ultrawideband-Based Location Fingerprinting Systems , 2011, IEEE Transactions on Signal Processing.

[4]  Ion Stoica,et al.  Blue-Fi: enhancing Wi-Fi performance using bluetooth signals , 2009, MobiSys '09.

[5]  Ruizhi Chen,et al.  Using Inquiry-based Bluetooth RSSI Probability Distributions for Indoor Positioning , 2011 .

[6]  Vladimir Pavlovic,et al.  Pose Invariant Activity Classification for Multi-floor Indoor Localization , 2014, 2014 22nd International Conference on Pattern Recognition.

[7]  Zhong-liang Deng,et al.  AP Selection for Indoor Localization Based on Neighborhood Rough Sets , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[8]  James Aweya,et al.  An Empirical Evaluation of a Probabilistic RF Signature for WLAN Location Fingerprinting , 2014, IEEE Transactions on Wireless Communications.

[9]  Rosdiadee Nordin,et al.  Leveraging existing WLAN infrastructure for wireless indoor positioning based on fingerprinting and clustering technique , 2014, 2014 International Conference on Electronics, Information and Communications (ICEIC).

[10]  Bing-Fei Wu,et al.  Particle-Filter-Based Radio Localization for Mobile Robots in the Environments With Low-Density WLAN APs , 2014, IEEE Transactions on Industrial Electronics.

[11]  Tong Wu,et al.  Analysis of K-Means algorithm on fingerprint based indoor localization system , 2013, 2013 5th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications.

[12]  J. Romme,et al.  Measurement and analysis of UWB radio channel for indoor localization in a hospital environment , 2014, 2014 IEEE International Conference on Ultra-WideBand (ICUWB).

[13]  Haiyong Luo,et al.  An RSSI gradient-based AP localization algorithm , 2014, China Communications.

[14]  Luigi Bruno,et al.  A model-based approach for WLAN localization in indoor parking areas , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.