Skip to main content
Log in

Nonlinear Filtering of Non-Gaussian Noise

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the new filter outperforms currently used approaches and at the same time offers simplicity in the design.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Candy, J. V.: Signal Processing: The Model-Based Approach, McGraw-Hill, New York, 1986.

    Google Scholar 

  2. Pitas, I. and Venetsanopoulos, A. N.: Nonlinear Digital Filters: Principles and Applications, Kluwer Academic Publishers, Norwell, MA, 1990.

    Google Scholar 

  3. Huber, P. J.: Robust estimation of a location parameter, Ann. Math. Statist. 35(1964), 73–101.

    Google Scholar 

  4. Middleton, D.:Man-made noise in urban environments and transportation systems, IEEE Trans. on Communications COM-21(1973), 1232–1241.

    Google Scholar 

  5. Middleton, D.: Canonical non-Gaussian noise models: Their implication for measurement and for prediction of receiver performance, IEEE Trans. Electromagn. Compat. EMC-21(1979), 209–219.

    Google Scholar 

  6. Achieser, N. I.: Theory of Approximation, translated by C. T. Hyman, Frederic Ungar, New York, 1956.

  7. Namera, T. and Stubberud, A. L.: Gaussian sum approximation for nonlinear fixed-point prediction, Internat. J. Control 38(5) (1983), 1047–1053.

    Google Scholar 

  8. Vastola, K. S.: Threshold detection in narrowband non-Gaussian noise, IEEE Trans. on Communications COM-32(1984), 134–139.

    Google Scholar 

  9. Garth, L. M. and Poor, H. V.: Narrowband interference suppression in impulsive channels, IEEE Trans. on Aerospace and Electronic Systems AES-28(1992), 15–35.

    Google Scholar 

  10. Sorenson, H. and Alspach, D. L.: Recursive Bayesian estimation using Gaussian sum, Automatica 7(1971), 465–479.

    Google Scholar 

  11. Alspach, D. L. and Sorenson, H.: Nonlinear Bayesian estimation using Gaussian sum approximations, IEEE Trans. on Automatic Control AC-17(4) (1972), 439–448.

    Google Scholar 

  12. Masreliez, C. J.: Approximate non-Gaussian filtering with linear state and observation relations, IEEE Trans. on Automatic Control AC-20(1975), 107–110.

    Google Scholar 

  13. Masreliez, C. J. and Martin, R. D.: Robust Bayesian estimation for the linear model and robustifying the Kalman filter, IEEE Trans. on Automatic Control AC-22(1977), 361–371.

    Google Scholar 

  14. Wu, Weng-Rong and Yu, F. F.: New nonlinear algorithms for estimating and suppressing narrowband interference in DS spread spectrum systems, IEEE Trans. on Communications 44(4) (1996), 508–515.

    Google Scholar 

  15. Fukunaga, K.: Introduction to Statistical Pattern Recognition, Second Edition, Academic Press, London, 1990.

    Google Scholar 

  16. Vijayan, R. and Poor, H. V.: Nonlinear techniques for interference suppression in spreadspectrum systems, IEEE Trans. on Communications COM-38(1990), 1060–1065.

    Google Scholar 

  17. Plataniotis, K. N.: Distributed parallel processing state estimation algorithms, PhD Dissertation, Florida Institute of Technology, Melbourne, Florida, 1994.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Plataniotis, K.N., Androutsos, D. & Venetsanopoulos, A.N. Nonlinear Filtering of Non-Gaussian Noise. Journal of Intelligent and Robotic Systems 19, 207–231 (1997). https://doi.org/10.1023/A:1007974400149

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1007974400149

Navigation