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Quality Assurance / Quality Control Analysis of Dead Reckoning Parameters in a Personal Navigator

  • Shahram Moafi poor
  • Dorota A. Grejner-Brzezinska
  • Charles K. Toth
  • Chris Rizos
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The personal navigator (PN) prototype, developed at The Ohio State University Satellite Positioning and Inertial navigation (SPIN) Laboratory, integrates GPS, tactical grade inertial measurement unit (IMU), digital magnetometer compass, digital barometer, and uses a human locomotion model to support dead reckoning (DR) navigation for rescue work, security and emergency services, police safety and military applications. The human locomotion model is represented here by the step length (SL) and step direction (SD). In the absence of GPS signals, the SL is predicted by a knowledge-based system (KBS) in the form of artifi cial neural network (ANN) and fuzzy logic (FL), while the SD is directly measured by the magnetometer and gyro IMU and modeled by a special module of a Kalman Filter, referred to as DR-KF. If the duration of a GPS outage is prolonged, the gyro and to some extent the magnetometer sensor errors will increase due to lack of updated calibration parameters that may result in an unacceptable level of navigation error.

The current target accuracy of the system is 3–5 m circular error probable, 50% (CEP), where the navigation performance depends predominantly on the quality of SD estimation. In this paper, a quality assurance/quality control (QA/QC) mechanism is introduced to test methodologies that predict SL and SD parameters and to monitor their integrity during DR navigation. The QA/QC process proposed here includes fully automated data processing for verifi cation of the measurement and other data content acquiescence and detecting outliers, essential for rejecting incorrectly attributed DR parameters; all these processes are performed in real-time. if

Keywords

personal navigator quality assurance / quality control knowledge-based system data validation 

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References

  1. Beauregard S (2006) A Helmet-Mounted Pedestrian Dead Reckoning System. Mobile Research Center, TZI Universität, IFAWC2006, March 15-16, Bremen, Germany, pp. 79-89Google Scholar
  2. Bhatti UI (2007) Improved integrity algorithms for integrated GPS/INS systems in the presence of slowly growing error.http://www.cts.cv.ic.ac.uk/documents/theses/BhattiPhD. pdf) (accessed 3 July. 2008).
  3. Cothren J (2005) Reliability in Constrained Gauss-Markov Models: An Analytical and Differential Approach with Applications in Photogrammetry. Geodetic Science and Surveying Report number 473 (http://www.ceegs.ohio-state.edu/gsreports/), The Ohio State University, Columbus, OH.
  4. Ding Z, Leung H, Chan K (2000) Model-set Adaptation using a Fuzzy Kalman Filter, Proceedings of the 3rd International Conference on Information Fusion, Paris, France, pp. 3-9Google Scholar
  5. Elkaim GH, Foster CC (2006) MetaSensor: Development of a Low-Cost, High Quality Attitude Heading reference System, Proceedings of the ION GNSS Meeting, September 26 - 29, Fort Worth, Texas, USA, pp. 1124 - 1135.Google Scholar
  6. Grejner-Brzezinska DA, Toth CK, Moafi poor S, Jwa Y (2006a) Multi-Sensor Personal Navigator Supported by Human Motion Dynamics Model, Proceedings of the 3rd IAG Symposium on Geodesy for Geotechnical and Structural Engineering/12th FIG Symposium on Deformation Measurements, May 2006, Baden, Austria, CD ROM.Google Scholar
  7. Grejner-Brzezinska DA, Toth CK, Moafi poor S, Jwa Y, Kwon J (2006b) A Low Cost Multi-Sensor Personal Navigator: System Design and Calibration. Presented at IEEE/ION PLANS Meeting, April 25-27, San Diego, CA, USA.Google Scholar
  8. Grejner-Brzezinska DA, Toth CK, Jwa Y, Moafi poor S (2006c) Seamless and Reliable Personal Navigator. Proceedings of the ION Technical Meeting, January 18-20, Monterey, CA, USA, CD ROM, pp. 597-603.Google Scholar
  9. Grejner-Brzezinska DA, Toth CK, Moafi poor S (2007a) Adaptive Knowledge-based System for Personal Navigation in GPS-Denied Environments, Proceedings of the ION National Technical Meeting, January 22-24, San Diego, CA, USA, CD ROM, pp.517-521.Google Scholar
  10. Grejner-Brzezinska DA, Toth CK, Moafi poor S (2007b) Pedestrian Tracking and Navigation using Adaptive Knowledge System based on Neural Networks and Fuzzy Logic, Journal of Applied Geodesy, vol. 1, No. 3, pp. 111-123, 2007, invited.CrossRefGoogle Scholar
  11. Hausdorff JM, Ashkenazy Y, Peng CK, Ivanov PC, Stanley HE, Goldberger AL (2001) When Human Walking Becomes Random Walking: Fractal Analysis and Modeling of Gait Rhythm Fluctuations, PHYSICA A, Vol. 302, pp.138-147.Google Scholar
  12. Hewitson S, Wang J (2006) GNSS Receiver Autonomous Integrity Monitoring (RAIM) with a dynamic model. Accepted for publication in the Journal of Navigation. (8 Nov. 2006)Google Scholar
  13. Juran JM, Godfrey AB (2000) Juran’s Quality Handbook, McGraw-Hill International Editions: Industrial Engineering Series.Google Scholar
  14. Ladetto Q, Merminod B (2002) Digital magnetic compass and gyroscope integration for pedestrian navigation. 9th Saint Petersburg International Conference on Integrated Navigation Systems, Saint Petersburg, Russia, May 2002.Google Scholar
  15. Ladetto Q, Seeters J van, Sokolowski S, Sagan A, Merminod B (2002) Digital Magnetic Compass and Gyroscope for Dismounted Soldier Position & Navigation, in NATO-RTO meetings, Istanbul, Turkey, October 2002.Google Scholar
  16. Mehra R, Bayard D (1995) Adaptive Kalman Filtering, Failure Detection and Identifi cation for Spacecraft Attitude Estimation, Proceedings of the 4th IEEE Conference on Control Applications, September 28, Albany, NY, USA, pp.176-181.Google Scholar
  17. Moafi poor S, Grejner-Brzezinska DA, Toth CK (2007a) A Fuzzy Dead Reckoning Algorithm for a Personal Navigator, Proceedings of the ION GNSS Meeting, September 2007, Fort Worth, Texas, USA, CD-ROM, 2007, pp.48-59, in review for NAVIGATION.Google Scholar
  18. Moafi poor S, Grejner-Brzezinska DA, Toth CK (2007b) Adaptive Calibration of a Magnetometer Compass for a Personal Navigation System, Proceedings of the International global Navigation Satellite Systems Society (IGNSS) Symposium, the University of New South Wales, December 2007, Sydney, Australia, CD ROM.Google Scholar
  19. Moafi poor S (2008) Adaptive Kalman Filtering for Personal Navigation System, Presented at the 2nd Annual Workshop of the Consortium of Ohio Universities on Navigation and Timekeeping (COUNT), April 7-8, The Ohio State University, USA.Google Scholar
  20. Moafi poor S, Grejner-Brzezinska DA, Toth CK (2008) Multi-Sensor Personal Navigator Supported by Adaptive Knowledge Based System: Performance Assessment, Proceeding of the IEEE/ION PLANS 2008 Meeting, May 5-8, in Monterey, California, USA, CD ROM.Google Scholar
  21. Retscher G (2004) Multi-sensor Systems for Pedestrian Navigation, Proceedings of the ION GNSS 17th International Technical Meeting of the Satellite Division, September 21-24, Long Beach, CA, USA, CD-ROM.Google Scholar
  22. Sasiadek JZ, Wang Q, Zeremba MB (2000) Fuzzy Adaptive Kalman Filtering for INS/ GPS Data Fusion, Proceedings of the 15th IEEE International Symposium on Intelligent Control (ISIC 2000), July 17-19, Rio Patras, Greece, pp. 181-186.Google Scholar
  23. Schaffrin B (2006) GS765: Network Analysis. Lecture notes, The Ohio State University, unpublished.Google Scholar
  24. Snow KB (2002) Applications of Parameter Estimation and Hypothesis Testing to GPS Network Adjustments, Geodetic Science and Surveying Report number 465 (http://www. ceegs.ohio-state.edu/gsreports/), The Ohio State University, Columbus, OH.
  25. Toth CK, Grejner-Brzezinska DA, Moafi poor S (2007) Pedestrian Tracking and Navigation using Neural Networks and Fuzzy Logic, Proceedings, IEEE International Symposium on Intelligent Signal Processing, Alcala De Henares, October 2007, Madrid, Spain, CD ROM, pp. 657-662.Google Scholar
  26. Yi Y, Grejner-Brzezinska DA (2005) Non-Linear Bayesian Filter: Alternative to the Extended Kalman Filter in the GPS/INS Fusion Systems, Proceedings of the ION GNSS, September 13-16, Long Beach, CA, pp.1391-1401.Google Scholar
  27. Yi Y (2007) On Improving the Accuracy and Reliability of GPS/INS-based Direct Sensor Georeferencing, Geodetic Science and Surveying Report number 484 (http://www.ceegs. ohio-state.edu/gsreports/), The Ohio State University, Columbus, OH.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shahram Moafi poor
    • 1
  • Dorota A. Grejner-Brzezinska
    • 1
  • Charles K. Toth
    • 1
  • Chris Rizos
    • 2
  1. 1.Department of Civil and Environmental Engineering and Geodetic Science Satellite Positioning and Inertial Navigation (SPIN) LaboratoryThe Ohio State UniversityColumbusUSA
  2. 2.School of Surveying and Spatial Information SystemsUniversity of New South WalesSydneyAustralia

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