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2022 Journal Impact Factor - 0.6
2022 CiteScore - 1.7




ISSN 2083-6473
ISSN 2083-6481 (electronic version)




Associate Editor
Prof. Tomasz Neumann

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
Data Integration from GPS and Inertial Navigation Systems for Pedestrians in Urban Area
1 Gdańsk University of Technology, Gdańsk, Poland
ABSTRACT: The GPS system is widely used in navigation and the GPS receiver can offer long-term stable absolute positioning information. The overall system performance depends largely on the signal environments. The position obtained from GPS is often degraded due to obstruction and multipath effect caused by buildings, city infrastructure and vegetation, whereas, the current performance achieved by inertial navigation systems (INS) is still relatively poor due to the large inertial sensor errors. The complementary features of GPS and INS are the main reasons why integrated GPS/INS systems are becoming increasingly popular. GPS/INS systems offer a high data rate, high accuracy position and orientation that can work in all environments, particularly those where satellite availability is restricted. In the paper integration algorithm of GPS and INS systems data for pedestrians in urban area is presented. For data integration an Extended Kalman Filter (EKF) algorithm is proposed. Complementary characteristics of GPS and INS with EKF can overcome the problem of huge INS drifts, GPS outages, dense multipath effect and other individual problems associated with these sensors.
Malleswaran M., Angel Deborah S., Manjula S., Vaidehi V. 2010. Integration of INS and GPS Using Radial Basis Function Neural Networks for Vehicular Navigation. 11th Int. Conf. Control, Automation, Robotics and Vision. Singapore, Malaysia.
Grewal M. S., Weill L. R. Andrews A. P. 2001. Global positioning system and inertial navigation, Wiley. New York, USA.
Feliz R., Zalama E., Garcia-Bermejo J. G. 2009. Pedestrian tracking using inertial sensors. Journal of Physical Agents Vol. 3, No. 1.
Maenaka K. 2008. MEMS inertial sensors and their applications. 5th international conference on networked sensing systems. Kanazawa, Japan.
Abdelkrim N. Nabil A. 2010. Robust INS/GPS sensor fusion for UAV localization using SDRE nonlinear filtering. IEEE Sensors Jurnal Vol. 10, No. 4.
Grejner-Brzezinska D. A., Toth Ch. K., Sun H., Wang X., Rizos Ch. 2011. A Robust Solution to High-Accuracy Geolocation: Quadruple Integration of GPS, IMU, Pseudolite, and Terrestrial Laser Scanning. IEEE Transactions on Instrumentation and Measurement Vol. 60, No. 11.
Hide Ch., Moore T., Smith M. 2004. Adaptive Kalman Filtering algorithms for integrating GPS and low cost INS. Position Location and Navigation Symposium. Monterey, USA.
Jimenez A.R., Seco F., Prieto C., Guevara J. 2009. A comparison of pedestrian dead-recording algorithms using a low-cost MEMS IMU. 6th International Symposium on Intelligent Signal Processing. Budapest, Hungary.
Weinberg H. 2002. Using the ADXL202 in Pedometer and Personal NavigationApplications. Analog Devices AN-602 application Note.
Ling Ch., Housheng H. 2012. IMU/GPS Based Pedestrian Localization. 2012 4th Computer Science and Electronic Engineering Conference (CEEC). University of Essex, UK.
Leutenegger S., Siegwart R. Y. 2012. A Low-Cost and Fail-Safe Inertial Navigation System for Airplanes. 2012 IEEE Conference on Robotics and Automation. Saint Paul, Minnesota, USA.
Kedzierski J. 2007. Filtr Kalmana – zastosowania w prostych układach sensorycznych. Koło naukowe robo tyków KoNaR. Wroclaw University of Technology, Poland.
Citation note:
Bikonis K., Demkowicz J.: Data Integration from GPS and Inertial Navigation Systems for Pedestrians in Urban Area. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 7, No. 3, doi:10.12716/1001.07.03.12, pp. 401-406, 2013

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