Curvature nonlinearity measure and filter divergence detector for nonlinear tracking problems

Ruixin Niu, Pramod K. Varshney, Mark Alford, Adnan Bubalo, Eric Jones, Maria Scalzo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Scopus citations

Abstract

Several nonlinear filtering techniques are investigated for nonlinear tracking problems. Experimental results show that for a weakly nonlinear tracking problem, the extended Kalman filter and the unscented Kalman filter are good choices, while a particle filter should be used for problems with strong nonlinearity. To quantitatively determine the nonlinearity of a nonlinear tracking problem, we propose two types of measures: one is the differential geometry curvature measure and the other is based on the normalized innovation squared (NIS) of the Kalman filter. Simulation results show that both measures can effectively quantify the nonlinearity of the problem. The NIS is capable of detecting the filter divergence online. The curvature measure is more suitable for quantifying the nonlinearity of a tracking problem as determined via simulations.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th International Conference on Information Fusion, FUSION 2008
DOIs
StatePublished - Dec 1 2008
Event11th International Conference on Information Fusion, FUSION 2008 - Cologne, Germany
Duration: Jun 30 2008Jul 3 2008

Publication series

NameProceedings of the 11th International Conference on Information Fusion, FUSION 2008

Other

Other11th International Conference on Information Fusion, FUSION 2008
CountryGermany
CityCologne
Period6/30/087/3/08

Keywords

  • Extended Kalman filter
  • Nonlinearity measures
  • Particle filter
  • Tracking
  • Unscented Kalman filter

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Niu, R., Varshney, P. K., Alford, M., Bubalo, A., Jones, E., & Scalzo, M. (2008). Curvature nonlinearity measure and filter divergence detector for nonlinear tracking problems. In Proceedings of the 11th International Conference on Information Fusion, FUSION 2008 [4632389] (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008). https://doi.org/10.1109/ICIF.2008.4632389