A Fuzzy Dead Reckoning Algorithm for a Personal Navigator

: A concept design of a fuzzy Dead Reckoning (DR) algorithm for a personal navigator (PN) is introduced here. The PN system prototype includes a range of self-contained sensors such as GPS, accelerometer, gyroscope, magnetometer, digital barometer, and step sensors. In addition, a human locomotion model is considered as a navigation sensor, with the step length (SL) and step direction (SD) as primary parameters. The major focus of this paper is on DR navigation supported by human dynamics during GPS signal blockages. It is demonstrated that in the absence of GPS, the other sensors can sense the body locomotion in terms of its dynamics and geometry that represent an implicit function of SL and SD. A practical implementation of the DR system based on human dynamics is a fuzzy logic Knowledge-Based System (KBS). This paper discusses the design and implementation of the KBS, followed by its performance evaluation in the indoor environments.

[1]  Charles K. Toth,et al.  MULTI-SENSOR PERSONAL NAVIGATOR SUPPORTED BY HUMAN MOTION DYNAMICS MODEL , 2006 .

[2]  Günther Retscher,et al.  NAVIO – A Navigation and Guidance Service for Pedestrians , 2004 .

[3]  K. Newell,et al.  Walking speed influences on gait cycle variability. , 2007, Gait & posture.

[4]  Shahram Moafipoor Adaptive Calibration of a Magnetometer Compass for a Personal Navigation System , 2007 .

[5]  Hui Fang,et al.  Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience , 2005, IEEE Transactions on Instrumentation and Measurement.

[6]  Jeffrey M. Hausdorff,et al.  Footswitch system for measurement of the temporal parameters of gait. , 1995, Journal of biomechanics.

[7]  Gérard Lachapelle,et al.  MEMS-IMU for Personal Positioning in a Vehicle - A Gyro-Free Approach , 2002 .

[8]  Kenji Mase,et al.  A Personal Indoor Navigation System using Wearable Sensors , 2001 .

[9]  Katsunori Ikoma,et al.  Obituary: Yukio Mano (1943–2004) , 2005, Journal of NeuroEngineering and Rehabilitation.

[10]  S. Miyazaki,et al.  Long-term unrestrained measurement of stride length and walking velocity utilizing a piezoelectric gyroscope , 1997, IEEE Transactions on Biomedical Engineering.

[11]  Jang Gyu Lee,et al.  A Personal Navigation System Using Low-Cost MEMS/GPS/Fluxgate , 2003 .

[12]  D. Landis,et al.  A Deep Integration Estimator for Urban Ground Navigation , 2006, 2006 IEEE/ION Position, Location, And Navigation Symposium.

[13]  Bradford W. Parkinson,et al.  Development of Indoor Navigation System using Asynchronous Pseudolites , 2000 .

[14]  C.K. Toth,et al.  Multi-sensor personal navigator supported by adaptive knowledge based system: Performance assessment , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[15]  Takeshi Kurata,et al.  Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[16]  Takeshi Kurata,et al.  Indoor/Outdoor Pedestrian Navigation with an Embedded GPS/RFID/Self-contained Sensor System , 2006, ICAT.

[17]  Kamiar Aminian,et al.  Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. , 2002, Journal of biomechanics.

[18]  Charles K. Toth,et al.  Adaptive Knowledge-based System for Personal Navigation in GPS-denied Environments , 2007 .

[19]  Charles K. Toth,et al.  Multi-Sensor Personal Navigator: System Design and Calibration , 2006 .

[20]  Dorota A. Grejner-Brzezinska,et al.  Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks , 2007 .