DOI QR코드

DOI QR Code

GPS Integrity Monitoring Method Using Auxiliary Nonlinear Filters with Log Likelihood Ratio Test Approach

  • Ahn, Jong-Sun (Department of Aerospace Information Engineering, Konkuk University) ;
  • Rosihan, Rosihan (Department of Aerospace Information Engineering, Konkuk University) ;
  • Won, Dae-Hee (Department of Aerospace Information Engineering, Konkuk University) ;
  • Lee, Young-Jae (Department of Aerospace Information Engineering, Konkuk University) ;
  • Nam, Gi-Wook (Department of Satellite Navigation, Space Application and Future Technology Center in Korea Aerospace Research Institute (KARI)) ;
  • Heo, Moon-Beom (Department of Satellite Navigation, Space Application and Future Technology Center in Korea Aerospace Research Institute (KARI)) ;
  • Sung, Sang-Kyung (Department of Aerospace Information Engineering, Konkuk University)
  • Received : 2010.07.26
  • Accepted : 2011.01.18
  • Published : 2011.07.01

Abstract

Reliability is an essential factor in a navigation system. Therefore, an integrity monitoring system is considered one of the most important parts in an avionic navigation system. A fault due to systematic malfunctioning definitely requires integrity reinforcement through systematic analysis. In this paper, we propose a method to detect faults of the GPS signal by using a distributed nonlinear filter based probability test. In order to detect faults, consistency is examined through a likelihood ratio between the main and auxiliary particle filters (PFs). Specifically, the main PF which includes all the measurements and the auxiliary PFs which only do partial measurements are used in the process of consistency testing. Through GPS measurement and the application of the autonomous integrity monitoring system, the current study illustrates the performance of the proposed fault detection algorithm.

References

  1. Greg Welch and Gray Bishop, "An Introduction to the Kalman Filter," UNC-Chapel Hill, TR 95-041, 2006.
  2. B. D. O. Anderson and J. B. Moore, Optimal Filtering. Prentice-hall, Englewood Cliffs, NJ, 1979.
  3. M. Sanjeev Arulampalam, Simmon Maskell, Neil Gordon, and Tim Clapp, "A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking," IEEE Transaction on signal processing, vol. 50, no. 2, Feb. 2002. https://doi.org/10.1109/78.978374
  4. B. Hofmann-Wellenhof, H. Lichtenegger and E. Wasle, GNSS GPS, GLONASS, Galileo and More. Springer, New York, 2007.
  5. DA, R.: "Failure detection of dynamical systems with the state chi-square test," Jr. Guid. Control Dyn., 1994, vol. 17, no. 2, pp. 271-277. https://doi.org/10.2514/3.21193
  6. J. J. Spilker, "GPS signal Structure and Performance Characteristics. Navigation," Journal of the Institute of Navigation, vol. 25 no. 2, pp. 121-146.
  7. B. Azimi-Sadjadi and P. S. Krishnaprasad, "Change detection for nonlinear systems: A particle filtering approach," in Proceedings of Amer. Control Conf., Anchorage, AK, 2002.
  8. X. X. Jin, "Algorithm for Carrier-Adjusted DGPS Positioning and Some Numerical Results," Journal of Geodesy, pp. 411-423, 1997.
  9. P. Li and V. Kadirkamanathan, "Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems," IEEE Trans. Syst., Man, Cybern. C, vol. 31, pp. 337-343, Aug. 2001. https://doi.org/10.1109/5326.971661
  10. A. Doucet, S. Godsill, and C. Ardrieu, "On sequential Monte-Carlo sampling methods for Bayesian filtering," Statist. Comput., vol. 10, pp. 197-208, 2000. https://doi.org/10.1023/A:1008935410038
  11. Rosihan, Arif Indryatmoko, Sebum Chun, Dae Hee Won, Young Jae Lee, Taesam Kang, Jeongrae Kim, Hyan-sig Jun. "Particle Filtering Approach to Fault Detection and Isolation for GPS Integrity Monitoring," in Proceedings of ION GNSS 19th International Technical Meeting, Sep. 2006, Fort Worth, TX
  12. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," Proc. Inst. Elect. Eng. F, vol. 140, no. 2, pp. 107-113, 1993.
  13. B. W. Parkinson, and P. Axelrad, "Autonomous GPS integrity monitoring using the pseudorange residual," Navig., J. Inst. Navig., vol. 35, no. 2, pp. 255-274, 1988. https://doi.org/10.1002/j.2161-4296.1988.tb00955.x
  14. J. C. Juang, and C. W. Jang, "A failure detection approach applying to GPS autonomous integrity monitoring," IEE Proc., Radar Sonar Navig., vol. 145, no. 6, pp. 342-346, 1998. https://doi.org/10.1049/ip-rsn:19982432
  15. Y. C. Lee, "Analysis or range and position comparison methods as a means to provide GPS integrity in the user receiver," in Proceedings of the Annual Meeting of the Institute of Navigation, Seattle, WA, Jun. 1986, pp. 1-4.
  16. D. H. Won, S. Chun, S. Sung, Y. J. Lee, J. Cho, J. Joo, J. Park, "INS/vSLAM System Using Distributed Particle Filter," International Journal of Control, Automation, and Systems, vol. 8, no. 6, 1232-1240, 2010. https://doi.org/10.1007/s12555-010-0608-7
  17. S. Feng, W. Y. Ochieng, T. Moore, and C. Hide, "Carrier Phase Based Integrity Monitoring for High Accuracy Positioning," GPS Solutions, vol. 13, no. 1, pp.13-22.

Cited by

  1. Performance Comparison of GPS Fault Detection and Isolation via Pseudorange Prediction Model based Test Statistics vol.7, pp.5, 2012, https://doi.org/10.5370/JEET.2012.7.5.797
  2. Nonlinear fault detection threshold optimization method for RAIM algorithm using a heuristic approach vol.20, pp.4, 2016, https://doi.org/10.1007/s10291-015-0494-9
  3. Multitarget Tracking by Particle Filtering Based on RSS Measurement in Wireless Sensor Networks vol.11, pp.5, 2015, https://doi.org/10.1155/2015/837070
  4. Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm vol.61, pp.5, 2018, https://doi.org/10.1016/j.asr.2017.12.016