Improved EKF method of estimating locations with sudden high jumps in the measurement noise

Arie Berman, Joshua Dayan, Bernard Friedland

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Range and bearing navigation is commonly used in many autonomous systems such as guided vehicles and freely moving robots. The obtained measurements are accompanied by noise and possible errors, which may lead to wrong decisions of the control and guidance system. An improved method, over Kalman filter with standard gates, of filtering unexpected high measurement noise due to clutter, glittering, or shaking of the measuring system, featuring minimum computational effort and minimum time, is suggested. Optimal variable correlation gates around the predicted values of the signal states - the Autonomous Guided Vehicle or its target/obstacle position - reduce the unexpected noise effect. Values of measurements out of these gates are not considered and the integration of the prediction model for the tracked signal continues until a new measurement is received within the gate opening. The dimensions of the correlation gates are determined by filter predictions and measurement error variances that are related to the probability of the unexpected high measurement noise and its approximated covariances.

Original languageEnglish (US)
Pages (from-to)461-476
Number of pages16
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number4
StatePublished - Dec 2001

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering
  • Artificial Intelligence


  • Autonomous guided vehicle
  • Extended Kalman filter
  • High-jump noise
  • Range and angle measurement
  • Tracking
  • Validation gate


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