A Comparison between Vector Algorithm and CRSS Algorithms for Indoor Localization using Received Signal Strength

─ A comparison is presented between two indoor localization algorithms using received signal strength, namely the vector algorithm and the Comparative Received Signal Strength (CRSS) algorithm. Signal values were obtained using ray tracing software and processed with MATLAB to ascertain the effects on localization accuracy of radio map resolution, number of access points and operating frequency. The vector algorithm outperforms the CRSS algorithm, which suffers from ambiguity, although that can be reduced by using more access points and a higher operating frequency. Ambiguity is worsened by the addition of more reference points. The vector algorithm performance is enhanced by adding more access points and reference points while it degrades with increasing frequency provided that the statistical mean of error increased to about 60 cm for most studied cases. Index Terms ─ CRSS, indoor localization, ray tracing, RSS.

[1]  K. Sayrafian-Pour,et al.  Robust Indoor Positioning Based on Received Signal Strength , 2007, 2007 2nd International Conference on Pervasive Computing and Applications.

[2]  Maxim Shchekotov,et al.  Indoor localization methods based on Wi-Fi lateration and signal strength data collection , 2015, 2015 17th Conference of Open Innovations Association (FRUCT).

[3]  Kostas E. Bekris,et al.  On the feasibility of using wireless ethernet for indoor localization , 2004, IEEE Transactions on Robotics and Automation.

[4]  W. Yu,et al.  Environmental-Adaptive RSSI-Based Indoor Localization , 2009, IEEE Transactions on Automation Science and Engineering.

[5]  Lei Xie,et al.  A New Indoor Localization Method Based on Inversion Propagation Model , 2010, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM).

[6]  Fredrik Gustafsson,et al.  Fingerprinting Localization in Wireless Networks Based on Received-Signal-Strength Measurements: A Case Study on WiMAX Networks , 2010, IEEE Transactions on Vehicular Technology.

[7]  Lei Zhang,et al.  Variation of Received Signal Strength in Wireless Sensor Network , 2011, 2011 3rd International Conference on Advanced Computer Control.

[8]  Nadir Shah,et al.  An Experimental Study on the Behavior of Received Signal Strength in Indoor Environment , 2013, 2013 11th International Conference on Frontiers of Information Technology.

[9]  Sanjay Jha,et al.  Received signal strength indicator and its analysis in a typical WLAN system (short paper) , 2013, 38th Annual IEEE Conference on Local Computer Networks.

[10]  Sisongkham Phimmasean,et al.  Robustness of 3D indoor localization based on fingerprint technique in wireless sensor networks , 2013, 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[11]  Dawood Moeinfar,et al.  Design and Implementation of a Low-Power Active RFID for Container Tracking at 2.4 GHz Frequency , 2012, IOT 2012.

[12]  Yunhao Liu,et al.  Location, Localization, and Localizability: Location-awareness Technology for Wireless Networks , 2010 .

[13]  Hongwei Du,et al.  HILL: A Hybrid Indoor Localization Scheme , 2014, 2014 10th International Conference on Mobile Ad-hoc and Sensor Networks.

[14]  Peter S. Excell,et al.  A Comparison between Vector Algorithm and CRSS Algorithm for Indoor Localization , 2014 .

[15]  Raed A. Abd-Alhameed,et al.  Indoor localization using received signal strength , 2013, 2013 8th IEEE Design and Test Symposium.

[16]  Kamran Sayrafian-Pour,et al.  A robust model-based approach to indoor positioning using signal strength , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[17]  Xiao Song,et al.  Relative Localization in Wireless Sensor Networks for Measurement of Electric Fields under HVDC Transmission Lines , 2015, Sensors.