Reduction of ultrasonic indoor localization infrastructure based on the use of graph information

This paper presents a constrained navigation on a Metric Description Graph (MDG) based on the use of a H-Infinity Filter (H-∞) including the restriction on the graph as a fictitious observation. The main goal is to reduce the number of the required ultrasonic beacons for covering an extensive indoor area. This reduction of the localization infrastructure involves an increment of the error in the estimation of the position, which can be acceptable depending on the application (i. e. people navigation). This approach is especially useful when the target is navigating along corridors or narrow passing areas since it is not necessary an accurate position estimation. The proposal has been simulated in a localization area describing a real environment. It has been applied for different noise conditions of the relative information (odometry) comparing the performance between a traditional beacon infrastructure and the reduced one including the constraints of the MDG.

[1]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[2]  Dan Simon,et al.  A game theory approach to constrained minimax state estimation , 2006, IEEE Transactions on Signal Processing.

[3]  K. C. Ho,et al.  A simple and efficient estimator for hyperbolic location , 1994, IEEE Trans. Signal Process..

[4]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[5]  Henry Cox,et al.  On the estimation of state variables and parameters for noisy dynamic systems , 1964 .

[6]  Andrew G. Dempster,et al.  Errors in determinstic wireless fingerprinting systems for localisation , 2008, 2008 3rd International Symposium on Wireless Pervasive Computing.

[7]  Jesus Urena,et al.  Simultaneous mobile robot positioning and LPS self-calibration in a smart space , 2010, 2010 IEEE International Symposium on Industrial Electronics.

[8]  Seth J. Teller,et al.  The cricket compass for context-aware mobile applications , 2001, MobiCom '01.

[9]  Joel J. P. C. Rodrigues,et al.  WicLoc: An indoor localization system based on WiFi fingerprints and crowdsourcing , 2015, 2015 IEEE International Conference on Communications (ICC).

[10]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[11]  Gyu-In Jee,et al.  Efficient use of digital road map in various positioning for ITS , 2000, IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062).

[12]  Juan C. García,et al.  Extensive Ultrasonic Local Positioning System for navigating with mobile robots , 2013, 2013 10th Workshop on Positioning, Navigation and Communication (WPNC).

[13]  D. Powell,et al.  Land-vehicle navigation using GPS , 1999, Proc. IEEE.

[14]  Aura Ganz,et al.  Automatic generation of indoor navigation instructions for blind users using a user-centric graph , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Min Yu,et al.  Study of fingerprint location algorithm based on WiFi technology for indoor localization , 2014 .