Abstract
According to the measurement noise feature of GPS receiver and the sample impoverishment problem with the basic particle filter, an improved particle filter based on neural network algorithm is proposed. Using back-propagation (BP) neural network to adjust the particles with too high and too low weight, firstly, the larger weight particles are respectively splitted into two smaller weight particles. Then, abandoning the particles with very small weight, and adjust the particles with smaller weight by using the neural network. Therefore, the diversity of the sample particles is improved. The improved particle filter algorithm is combined with the likelihood ratio method for GPS receiver autonomous integrity monitoring (RAIM). By using the likelihood ratio as a consistency test statistic to achieve the fault detection, satellite fault detection is undertaken by checking the cumulative likelihood ratio of system state with detection threshold. By taking advantage of the relationship in statistical values between the total cumulative likelihood ratio and partial cumulative likelihood ratio, the number of fault satellite can be determined. Based on the real GPS raw data, the simulation results demonstrate that the improved particle filter under the conditions of non-Gaussian measurement noise can effectively detect and isolate fault satellite, and improve the performance of fault detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xu XH, Yang CS, Liu RH (2013) Review and prospect of GNSS receiver autonomous integrity monitoring[J]. Acta Aeronautica et Astronautica Sinica 34(3):451–463 (in Chinese)
Yun Y, Kim D (2007) Integrity monitoring algorithms using filtering approaches for higher navigation performance:consideration of the non-gaussian gnss measurements[C]. In: Proceedings of ION GNSS 20th international technical meeting of the satellite division, Fort Worth, pp 3070–3071
Sun GL, Sun MH, Chen JP (2006) A study on time and set combined method for receiver integrity autonomous monitoring. Acta Aeronautica et Astronaut Sin 27(6):1171–1175. (in Chinese)
Mathieu J, Boris P (2011) Integrity risk of kalman filter-based RAIM[C]. In: Proceedings of the 24th international technical meeting of the satellite division of the institute of navigation. USA:ION, 3856–3867
Sayim I, Pervan B, Pullen S, Enge P (2002) Experimental and theoretical results on the LAAS sigma overbound. In: Proceedings of the ION GPS, Portland, pp 29–38
Gordonn J, Almond S, Looney DJ et al (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proc F Radar Sig Process 140(2):107–113
Vaswani N (2004) Bound on errors in particle filtering with incorrect model assumptions and its implication for change detection. Proceedings of IEEE international conference on acoustics, speech and signal processing, Montreal, II-729–32
F Gustafsson, F Gunnarsson et al (2002) Particle filters for positioning, navigation, and tracking. IEEE Trans Sig Process 50(2):425–437
Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188
Chen Y,Wang L, Huang S (2006) Neural network learning algorithm based on particle filter. Eng J Wuhan Univ 39(6):86–88 (in Chinese)
Kaplan E, Hegarty C (2006) Understanding GPS: principles and application, 2nd edn. Artech House, Norwood
Li P, Kadirkamanathan V (2001) Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems. IEEE Trans Syst Man Cybern C 31(3):337–343
Rosihan R, Indriyatmoko A, Chun S et al (2007) Particle filtering approach to fault detection and isolation for GPS integrity monitoring. In: Proceedings of ION GNSS 19th international technical meeting of the satellite division, Fort Worth, pp 873–881
Acknowledgment
This study is funded by National Natural Science Foundation of China(61101161), The Aeronautical Science Foundation of China(2011ZC54010) and the Joint Funds of the Natural Science Foundation of Liaoning Province(2013024003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, E., Pang, T., Cai, M., Zhang, Z. (2014). Application of Neural Network Aided Particle Filter in GPS Receiver Autonomous Integrity Monitoring. In: Sun, J., Jiao, W., Wu, H., Lu, M. (eds) China Satellite Navigation Conference (CSNC) 2014 Proceedings: Volume II. Lecture Notes in Electrical Engineering, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54743-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-54743-0_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54742-3
Online ISBN: 978-3-642-54743-0
eBook Packages: EngineeringEngineering (R0)