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Application of Bilinear Recursive Least Square Algorithm for Initial Alignment of Strapdown Inertial Navigation System

  • Bidhan MalakarEmail author
  • B. K. Roy
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

To improve the alignment accuracy and convergence speed of Strapdown inertial navigation system, an initial alignment which is based on Bilinear Recursive Least Square adaptive filter is proposed. The error model for the Strapdown Inertial Navigation System (SINS) is derived from the dynamic model by considering a small misalignment angle. In the literature many algorithms are proposed for the proper estimation of alignment accuracy for INS and it is still a challenge. In this paper, two algorithms which are mainly based on nonlinear adaptive filter viz. Volterra Recursive Least Square (VRLS) and Bilinear Recursive Least Square (BRLS) are proposed and compared for proper estimation of accurate azimuth alignment error. The comparative performances of the aforesaid algorithms are studied and the performance of proposed BRLS algorithm is found to be effective which is obtained in existence of two different white Gaussian noises. The simulation work is done in MATLAB simulating environment. For the realization and validation of proposed BRLS algorithm, the comparative analysis is also precisely presented.

Keywords

Adaptive Filter Body Frame Alignment Accuracy Initial Alignment Misalignment Angle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Titterton, D., Weston, J. In: Strapdown Inertial Navigation Technology, Institution of Electrical Engineers (2004)Google Scholar
  2. 2.
    Wang, X., Shen, G.: A Fast and Accurate Initial Alignment Method for Strapdown Inertial Navigation System on Stationary Base. Journal of Control Theory and Applications, 145–149 (2005)Google Scholar
  3. 3.
    Sun, F., Zhang, H.: Application of a New Adaptive Kalman Filitering Algorithm in Initial Alignment of INS. In: Proceedings of the IEEE International Conference on Information and Automation, Beijing, China, pp. 2312–2316 (2011)Google Scholar
  4. 4.
    Gong-Min, V., Wei-Sheng, Y., De-Min, X.: Application of simplified UKF in SINS initial alignment for large misalignment angles. Journal of Chinese Inertial Technology 16(3), 253–264 (2008)Google Scholar
  5. 5.
    Anderson, B.D.O., Moore, J.B.: Optimal Filtering. Prentice-Hall Inc., Englewood Cliffs (1979)zbMATHGoogle Scholar
  6. 6.
    Savage, P.G.: A unified mathematical framework for strapdown algorithm design. J. Guid. Contr. Dyn. 29, 237–249 (2006)CrossRefGoogle Scholar
  7. 7.
    Silson, P.M.: Coarse alignment of a ship’s strapdown inertial attitude reference system using velocity loci. IEEE Transcript Instrumentation Measurement (2011)Google Scholar
  8. 8.
    Li, Q., Ben, Y., Zhu, Z., Yang, J.: A Ground Fine Alignment of Strapdown INS under a Vibrating Base. J. Navigation 1, 1–15 (2013)Google Scholar
  9. 9.
    Wu, M., Wu, Y., Hu, X., Hu, D.: Optimization-based alignment for inertial navigation systems: Theory and algorithm. Aeros. Science & Technology, 1–17 (2011)Google Scholar
  10. 10.
    Salychev, O.S.: Applied Estimation Theory in Geodetic and Navigation Applications. Lecture Notes for ENGO 699.52, Department of Geomatics Engineering, University of Calgary (2000)Google Scholar
  11. 11.
    Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. In: Proc. of the IEEE Aerospace and Electronic Systems, pp. 401–422 (2004)Google Scholar
  12. 12.
    Paulo, S.R.: Adaptive Filtering Algorithms and Practical Implementation, 3rd edn. SpringerGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Institute of Technology SilcharSilcharIndia

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