[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Nonlinear filtering for map-aided navigation Part 2. Trends in the algorithm development

  • Published:
Gyroscopy and Navigation Aims and scope Submit manuscript

Abstract

This paper deals with the problem of map-aided navigation. Based on the previous overview of nonlinear filtering algorithms for this problem solution, current trends in the development of such algorithms are discussed. Some new lines in identification of error models and the use of information on the probable vehicle motion are considered.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Stepanov, O.A. and Toropov, A.B., Nonlinear filtering for map-aided navigation, Part 1. An overview of algorithms, Gyroscopy and Navigation, 2015, vol. 6, no. 4, pp. 324–337.

    Article  Google Scholar 

  2. Nørgaard, M., Poulsen, N.K., and Ravn, O., New developments in state estimation for nonlinear systems, Automatica, 2000, 36(11), pp. 1627–1638.

    Article  MathSciNet  Google Scholar 

  3. Chen, Z., Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond, 2003, http://www.-Fi (xi 1 ) MV MV xiMV dsi.unifi.it/users/chisci/idfric/Nonlinear_filtering_Ch en.pdf

  4. Li, X.R. and Jilkov, V.P., A survey of maneuvering target tracking: Approximation techniques for nonlinear filtering, Proc. SPIE Conference on Signal and Data Processing of Small Targets, 2004, pp. 537–550.

    Google Scholar 

  5. Juiler, S.J. and Uhlmann, J.K., Unscented filtering and nonlinear estimation, Proc. IEEE, 2004, vol. 92(3), pp. 401–422.

    Article  Google Scholar 

  6. Li, X.R., Recursibility, batch and recursive forms of optimal linear estimation and filtering, Proc. of the Workshop on Multisensor Target Tracking: A Tribute to Oliver E. Drummond, 2004, pp. 99–111.

    Google Scholar 

  7. Daum, F., Nonlinear filters: Beyond the Kalman filter, IEEE Aerospace and Electronic Systems, 2005, vol. 20(8), pp. 57–71.

    Article  Google Scholar 

  8. Metzger, J., Wisotzky, K., Wendel, J., and Trömmer, G.F., Sigma-point filter for terrain referenced navigation, Proc. of AIAA Guidance, Navigation and Control Conference, San Francisco, CA, August 2005.

    Google Scholar 

  9. Lefebvre, T., Bruyninckx, H., and de Schutter, J., Nonlinear Kalman Filtering for Force-Controlled Robot Tasks, 2005, Berlin: Springer.

    Book  MATH  Google Scholar 

  10. Simon, D. Optimal State Estimation. Kalman, H8, and Nonlinear approaches, A John Wiley & Sons, Inc., 2006.

    Book  Google Scholar 

  11. Stepanov, O.A., A linear optimal algorithm in nonlinear problems of navigation data processing, Giroskopiya i Navigatsiya, 2006, no. 4, pp. 11–20.

    Google Scholar 

  12. Ånonsen, K.B. and Hallingstad O., Sigma point Kalman filter for underwater terrain-based navigation, Control Applications in Marine Systems, 2007, vol. 7, Part 1, pp. 106–110.

    Google Scholar 

  13. Arasaratnam, I., Haykin, S., and Elliott, R.J., Discrete-time nonlinear filtering algorithms using Gauss–Hermite quadrature, Proc. IEEE, 2007, vol. 95, no. 5, pp. 953–977.

    Article  Google Scholar 

  14. Stepanov, O.A., Toropov, A.B., and Amosov, O.S., Comparison of Kalman-type algorithms in nonlinear navigation problems for autonomous vehicles, 6-th IFAC Symposium on Intelligent Autonomous Vehicles, 2007, pp. 493–498.

    Google Scholar 

  15. Arasaratnam, I. and Haykin, S., Cubature Kalman filters, IEEE Trans. on Automatic Control, 2009, vol. 54, no. 6, pp. 1254–1269.

    Article  MathSciNet  Google Scholar 

  16. Stepanov, O.A. and Toropov, A.B., Linear optimal algorithms in estimation problems with nonlinear measurements. Relation to Kalman-type algorithms. Izvestiya TulGU. Tekhnicheskii vestnik, 2012, no. 7, pp. 172–189.

    Google Scholar 

  17. Duník, J., Šimandl, M., and Straka, O., Unscented Kalman filter: Aspects and adaptive setting of scaling parameter, IEEE Trans, on Automatic Control, 2012, 57(9), pp. 2411–2416.

    Article  Google Scholar 

  18. Stano, P., Lendek, Z., Braaksma, J., Babuska, R., de Keizer, C. and den Dekker, A.J., Parametric Bayesian filters for nonlinear stochastic dynamical systems: A survey, IEEE Trans. on Cybernetics, 2013, vol. 43, no. 6, pp. 1607–1624.

    Article  Google Scholar 

  19. Stepanov, O.A., The basic approaches and methods used to solve applied problems of measurement information processing, Materialy XVI konferentsii molodykh uchenykh “Navigatsiya i upravlenie dvizheniem” (Proc. XVI Conference of Young Scientists “Navigation and Motion Control”), St. Petersburg: Elektropribor, 2014, pp. 12–35.

    Google Scholar 

  20. Zaritskii, V.S., Svetnik, V.B., and Shimelevich, L.I., Monte Carlo technique in problems of optimal data processing, Automation and Remote Control, 1975, vol. 12, pp. 95–103.

    MathSciNet  Google Scholar 

  21. Gordon, N.J., Salmond, D.J., and Smith, A.F.M., Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEEE Proceedings, Pt. F, 1993, vol. 140, no. 2, pp. 107–113.

    Google Scholar 

  22. Stepanov, O.A., Ivanov, A.M., and Korenevski, M.L., Monte Carlo methods for a special nonlinear filtering problem, 11th IFAC International Workshop Control Applications of Optimization, 2000, vol. 1, pp. 347–353.

    Google Scholar 

  23. Doucet, A., de Freitas, N. and Gordon, N.J., Sequential Monte Carlo Methods in Practice, New York: Springer-Verlag, 2001.

    Book  MATH  Google Scholar 

  24. Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., and Nordlund, P-J., Particle filters for positioning, navigation and tracking, IEEE Trans. on Signal Processing, 2002, vol. 50, no. 2, pp. 425–437.

    Article  Google Scholar 

  25. Arulampalam, M.S., Maskell, S., and Gordon, N., Tutorial on particle filters for on-line nonlinear/nonGaussian Bayesian tracking, IEEE Trans. on Signal Processing, 2002, vol. 50, no. 2, pp. 174–188.

    Article  Google Scholar 

  26. Metzger, J. and Trömmer, G.F., Studies on four terrain referenced navigation techniques, Symposium Gyro Technology, 2002, pp. 15.0–15.9.

    Google Scholar 

  27. Karlsson, R. and Gustafsson, F., Particle filter for underwater terrain navigation: Technical report, Report no.: LiTH-ISY-R-2530. Submitted to Statistical Signal Processing workshop 2003, http://www.control.isy.liu.se/publications.

  28. Flament, A., Fleury, G., and Davoust, M.-E., Particle filter and Gaussian-mixture filter efficiency evaluation for terrain-aided navigation, Proc. XII European Signal Processing Conference, 2004, pp. 605–608.

    Google Scholar 

  29. Schön, T., Gustaffson, F., and Nordlund, P.-J., Marginalized particle filters for linear/nonlinear statespace models, IEEE Trans. on Signal Processing, 2005, vol. 53, no. 7, pp. 2279–2289.

    Article  Google Scholar 

  30. Ånonsen, K.B. and Hallingstad, O., Terrain aided underwater navigation using point mass and particle filters, PLANS IEEE, 2006, pp. 1027–1035.

    Google Scholar 

  31. Gustafsson, F., Particle filter theory and practice with positioning applications, IEEE Aerospace and Electronic Systems Magazine, vol. 25, no. 7, 2010, pp. 53–82.

    Article  Google Scholar 

  32. Stepanov, O.A. and Toropov, A.B., Comparison of point mass and particle filters in map-aided navigation, 17th Saint Petersburg International Conference on Integrated Navigation Systems, 2010, pp. 381–384.

    Google Scholar 

  33. Doucet, A. and Johansen, A.M., A tutorial on particle filtering and smoothing: Fifteen years later, In: The Oxford Handbook of Nonlinear Filtering. Oxford Handbooks in Mathematics, Oxford: Oxford University Press, 2011, pp. 656–705.

    Google Scholar 

  34. Stepanov, O.A. and Toropov, A.B., Application of the Monte Carlo methods with partial analytical integration techniques to the problem of navigation system aiding, 20th Saint Petersburg International Conference on Integrated Navigation Systems, 2013, pp. 298–301.

    Google Scholar 

  35. Berkovskii N.A. and Stepanov, O.A., Studying the error in the calculation of the optimal Bayesian estimate by the Monte Carlo method in nonlinear problems, Izvestiya Rossiiskoi akademii nauk. Teoriya i sistemy upravleniya, 2013, vol. 52, no. 3, pp. 3–14.

    MathSciNet  Google Scholar 

  36. Särkkä, S., Bayesian Filtering and Smoothing, Cambridge University Press, 2013.

    Google Scholar 

  37. Stepanov, O.A. and Toropov, A.B., Application of sequential Monte Carlo methods with procedures of analytical integration to navigation data processing, Materialy XII Vserossiiskogo soveshchaniya po problemam upravleniya, (XII All-Russian Conference on Control), Moscow: V.A. Trapeznikov Institute of Control Sciences), 2014, pp. 3324–3337.

    Google Scholar 

  38. Magill, D.T., Optimal adaptive estimation sampled stochastic processes, IEEE Trans. on Automatic Control, Oct. 1965, vol. AC-IO, no. 4, pp. 434–439.

    Article  MathSciNet  Google Scholar 

  39. Lainiotis, D.G., Optimal adaptive estimation: Structure and parameter adaptation. IEEE Trans. Automatic Control, Apr. 1971, vol. 16, pp. 160–170.

    Article  MathSciNet  Google Scholar 

  40. Lainiotis, D.G., Partitioning: A unifying framework for adaptive systems, I: Estimation, II: Control, IEEE Trans., 1976, vol. 64, no. 8. I. Estimation, pp. 1126–1140. II: Control, pp. 1182–1198.

    MathSciNet  Google Scholar 

  41. Stepanov, O.A., Primenenie teorii nelineinoi fil’tratsii v zadachakh obrabotki navigatsionnoi informatsii (Application of Nonlinear Filtering Theory for Navigation Data Processing), St. Petersburg: Elektropribor, 1998.

    Google Scholar 

  42. Sobol’, I.M., Chislennye metody Monte-Karlo (Numerical Monte Carlo methods), Moscow: Nauka, 1973.

    MATH  Google Scholar 

  43. Ermakov, S.M., Metod Monte-Karlo i Smezhnye Voprosy (Monte Carlo methods and related issues), Moscow: Nauka, 1971.

    MATH  Google Scholar 

  44. Stepanov, O.A., Motorin A.V., Vasil’ev, V.A., and Toropov, A.B., Using nonlinear filtering methods for constructing error models of sensors and map, Materialy 29 konferentsii pamyati vydayushchegosya konstruktora giroskopicheskikh priborov N.N. Ostryakova (Proc. 29th Conf. in Memory of N.N. Ostryakov), St. Petersburg: Elektropribor, 2014, pp. 293–302.

    Google Scholar 

  45. Beloglazov, I.N. and Kazarin, S.N., Stereoscopic navigation systems, Izvestiya Rossiiskoi akademii nauk. Teoriya i sistemy upravleniya, 1997, no. 6, pp. 15–37.

    Google Scholar 

  46. Beloglazov, I.N. and Kazarin, S.N., Joint optimal estimation, identification, and testing of hypotheses in discrete dynamical systems, Izvestiya Rossiiskoi akademii nauk. Teoriya i sistemy upravleniya, 1998, no. 4, pp. 69–73.

    Google Scholar 

  47. Rozov, A.K., Nelineinaya fil’tratsiya signalov (Nonlinear Signal Filtering), Moscow: Politekhnika, 2002.

    Google Scholar 

  48. Beloglazov, I.N., Kazarin, S.N., Stereoscopic navigation systems and observations, Giroskopiya i Navigatsiya, no. 2 (29), 2000, pp. 70–87.

    Google Scholar 

  49. Dmitriev, S.P. and Stepanov, O.A., Multialternative filtering in navigation data processing, Radotekhnika, 2004, no.7, pp. 11–17.

    Google Scholar 

  50. Stepanov, O.A., Sokolov, A.I., and Dolnakova, A.S., Analyzing the potential accuracy in estimating the parameters of stochastic processes in navigation data processing, Materialy XII Vserossiiskogo soveshchaniya po problemam upravleniya, (XII All-Russian Conference on Control), Moscow: V.A. Trapeznikov Institute of Control Sciences), 2014, pp. 3324–3337.

    Google Scholar 

  51. Stepanov, O.A. and Motorin, A.V., Identification of sensor errors: Allan variance vs nonlinear filtering, Proc. 21st Saint Petersburg International Conference on Integrated Navigation Systems, St. Petersburg, 2014, pp.123–128.

    Google Scholar 

  52. Stepanov, O.A., Sokolov A.V., Toropov, A.B., Vasil’ev, V.A., and Krasnov, A.A., Choosing informative trajectories in the problem of correlation extreme navigation with consideration for errors of map and sensors, Materialy 29 konferentsii pamyati vydayushchegosya konstruktora giroskopicheskikh priborov N.N. Ostryakova (Proc. 29th Conf. in Memory of N.N. Ostryakov), St. Petersburg: Elektropribor, 2014, pp. 217–225.

    Google Scholar 

  53. Stepanov, O.A. and Motorin, A.V., Designing an error model for navigation sensors using the Bayesian approach, 2015 IEEE International Conference on Multisensor Fusion and lntegration for Intelligent Systems (MFI), Sept 14–16, 2015. San Diego, CA, USA, pp. 54–58.

    Google Scholar 

  54. Stepanov, O.A., Vasilyev, V.A., and Dolnakova, A.S., Cramer–Rao lower bound for parameters of random processes in navigation data processing, 21st Mediterranean Conference on Control & Automation (MED), Platanias–Chania, Crete, Greece, June 25–28, 2013, pp. 1214–1221.

    Chapter  Google Scholar 

  55. Koifman, M. and Bar–Itzhack, I., Inertial navigation system aided by aircraft dynamics, IEEE Trans. on Control Systems Technology 1999, 7 (4), pp. 487493.

    Article  Google Scholar 

  56. Canciani, A.J. and Raquet, J.F., Absolute positioning using the Earth’s magnetic anomaly field, Proc. Institute of Navigation 2015 International Technical Meeting, 2015, pp. 265–278.

    Google Scholar 

  57. Stepanov, O.A., Koshaev, D.A., and Motorin, A.V., Designing models for signals and errors of sensors in airborne gravimetry using nonlinear filtering methods, Proc. Institute of Navigation 2015 International Technical, 2015, pp. 279–284.

    Google Scholar 

  58. Brown R.G., Integrated navigation systems and Kalman filtering: A perspective, Navigation, USA, 1972–1973, vol.19, no. 4, pp. 355–362.

    Google Scholar 

  59. Brown, R.G. and Hwang, P.Y.C., Introduction to Random Signals and Applied Kalman Filtering. 2nd ed., Wiley, New York,1992.

    MATH  Google Scholar 

  60. Mansour, M.E.E. and Stepanov, O.A., Complementary filter in the problems of integrated processing of redundant measurement, Trudy Vserossiiskoi nauchnoi konferentsii po problemam upravleniya v tekhnicheskikh sistemakh (Proc. of All-Russian Scientific Conference on Problems of Control in Technical Systems, 2015, pp. 380–384.

    Google Scholar 

  61. Dmitriev, S.P. and Stepanov, O.A., Noninvariant algorithms for INS data processing, Giroskopiya i Navigatsiya, 2000, no. 1(30), pp. 24–38.

    Google Scholar 

  62. Krasovskii, A.A,, Beloglazov, I.N., and Chigin, G.P., Teoriya korrelyatsionno-ekstremal’nykh navigatsionnykh sistem (Theory of Correlation-Extreme Navigation Systems), Moscow: Nauka, 1979.

    Google Scholar 

  63. Dmitriev, S.P. and Koshaev, D.A., Estimation of continuously differentiable signal with allowance for constraints, Automation and Remote Control, 2011, 72:7, 1458–1473

    Article  MathSciNet  MATH  Google Scholar 

  64. Crocoll, P., Seibold, J., Scholz, G., and Trommer, G.F., Model-aided navigation for a quadrotor helicopter: A novel navigation system and first experimental results, Journal of the Institute of Navigation, vol. 61, no 4, 2014, pp. 253–271.

    Article  Google Scholar 

  65. Honkavirta, V., Perälä, T., Ali-Löytty, S., and Piché, R., A comparative survey of WLAN location fingerprinting methods, Proc. 6th Workshop on Positioning, Navigation and Communication 2009 (WPNC’09). Hannover, Germany, March 2009, pp. 243–251.

    Chapter  Google Scholar 

  66. Feder, H.J.S., Leonard, J.J., and Smith, C.M., Adaptive mobile robot navigation and mapping, Int. J. of Robotics Research, Special Issue on Field and Service Robotics, July 1999, vol. 18, no. 7, pp. 650–668.

    Google Scholar 

  67. Törnqvist, D, Schön, Th. B., Karlsson, R., and Gustafsson, F., Particle filter SLAM with high dimensional vehicle model. Journal of Intelligent and Robotic Systems, August 2009, 55(4-5), pp. 249–266.

    Article  MATH  Google Scholar 

  68. Ho, B. et al., Autonomous navigation for autonomous underwater vehicles based on information filters and active sensing, Sensors, 2011, no. 11, pp. 10958–10980.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Concern CSRI Elektropribor, JSC, ITMO University, St. Petersburg, Russia

    O. A. Stepanov & A. B. Toropov

Authors
  1. O. A. Stepanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. B. Toropov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to O. A. Stepanov.

Additional information

Published in Giroskopiya i Navigatsiya, 2015, No. 4, pp. 147–159.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stepanov, O.A., Toropov, A.B. Nonlinear filtering for map-aided navigation Part 2. Trends in the algorithm development. Gyroscopy Navig. 7, 82–89 (2016). https://doi.org/10.1134/S2075108716010132

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S2075108716010132

Keywords

Search

Navigation