Kalman Filtering with Adaptive Step Size Using a Covariance-Based Criterion

Barak Or, Ben Zion Bobrovsky, Itzik Klein

Research output: Contribution to journalArticlepeer-review

Abstract

In Kalman filtering (KF), a tradeoff exists when selecting the filter step size. Generally, a smaller step size improves the estimation accuracy, yet with the cost of a high computational load. To mitigate this tradeoff influence on performance, a criterion that acts as a guideline for a reasonable choice of the step size is proposed. This criterion is based on the predictor-corrector error covariance matrices of the discrete KF. In addition, this criterion is elaborated to an adaptive algorithm, for the case of the time-varying measurement noise covariance. Two simulation examples and a field experiment using a quadcopter are presented and analyzed to show the benefits of the proposed approach.

Original languageEnglish
Article number9366835
JournalIEEE Transactions on Instrumentation and Measurement
Volume70
DOIs
StatePublished - 2021

Keywords

  • Adaptive algorithm
  • Kalman filter (KF)
  • drones
  • step size
  • vehicle tracking

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