Curvature Nonlinearity Measure and Filter Divergence Detector for Nonlinear Tracking Problems (Postprint)

Abstract

Experimental results show that for a weekly 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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2008
Accession Number
ADA495987

Entities

People

  • Adnan Bubalo
  • Eric W. Jones
  • Maria Scalzo
  • Mark Alford
  • Pramod Varshney
  • Ruixin Niu

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Computational Complexity
  • Curvature
  • Detectors
  • Estimators
  • Filters
  • Filtration
  • Geometry
  • Kalman Filters
  • Mathematical Filters
  • Monte Carlo Method
  • Particles
  • Random Variables
  • Sequential Monte Carlo Methods
  • Simulations
  • Statistical Algorithms

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.