Direction-Aided Indoor Positioning Leveraging Ultra-Wideband Radio

Many emerging applications based on robotics or augmented reality (e.g. for museums, BIM...) require accurate indoor positioning, that obviously can not be achieved by global positioning systems or traditional radio systems using received signal strength indicators. Ultra-wideband radio is a promising technology to enhance this accuracy, but its robustness still suffers in noisy environment and most of localization algorithms keep a prohibitive complexity to be embedded on target nodes. The Newton-Gauss algorithm represents a good trade-off between positioning performance and processing needs at the node level. As some recent wireless body area networks embed inertial measurement unit, the target direction can be used to further enhance the accuracy while keeping an acceptable complexity. In fact, the direction allows to narrow the research area of the mobile position. Thanks to this additional information, our direction-aided Newton-Gauss algorithm allows a gain of more than 14% in terms of accuracy over classical Newton-Gauss algorithm.

[1]  Rong Peng,et al.  Angle of Arrival Localization for Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[2]  Jesus Urena,et al.  Efficient trilateration algorithm using time differences of arrival , 2013 .

[3]  Andreas Mitschele-Thiel,et al.  Comparison of Anchor Selection algorithms for improvement of position estimation during the Wi-Fi localization process in disaster scenario , 2012, 37th Annual IEEE Conference on Local Computer Networks.

[4]  Mohammad Ali,et al.  An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT) , 2016, 2016 International Conference on Computer and Communication Engineering (ICCCE).

[5]  F Gustafsson,et al.  Particle filter theory and practice with positioning applications , 2010, IEEE Aerospace and Electronic Systems Magazine.

[6]  Anil Misra,et al.  A Practical, Robust and Fast Method for Location Localization in Range-Based Systems , 2017, Sensors.

[7]  Mónica F. Bugallo,et al.  A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics , 2004, EURASIP J. Adv. Signal Process..

[8]  Ramón F. Brena,et al.  Evolution of Indoor Positioning Technologies: A Survey , 2017, J. Sensors.

[9]  Yu Zhou,et al.  An efficient least-squares trilateration algorithm for mobile robot localization , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Antoine Courtay,et al.  Cooperative-cum-Constrained Maximum Likelihood algorithm for UWB-based localization in wireless BANs , 2015, 2015 IEEE International Conference on Communications (ICC).

[11]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[12]  Fernando Seco,et al.  A survey of mathematical methods for indoor localization , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[13]  Rosdiadee Nordin,et al.  Recent Advances in Wireless Indoor Localization Techniques and System , 2013, J. Comput. Networks Commun..

[14]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[15]  Pau Closas,et al.  Bayesian filtering for indoor localization and tracking in wireless sensor networks , 2012, EURASIP J. Wirel. Commun. Netw..

[16]  Antoine Courtay,et al.  Zyggie: A Wireless Body Area Network platform for indoor positioning and motion tracking , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).