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.
|Number of pages||10|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|State||Published - 2021|
Bibliographical notePublisher Copyright:
© 1963-2012 IEEE.
- Adaptive algorithm
- Kalman filter (KF)
- step size
- vehicle tracking
ASJC Scopus subject areas
- Electrical and Electronic Engineering