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

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.

Automated summary

In Kalman filtering, there is a tradeoff between estimation accuracy and computational load when selecting the filter step size. A criterion based on error covariance matrices is proposed to guide a reasonable choice of step size, and an adaptive algorithm is elaborated for the case of time-varying measurement noise covariance. Simulation examples and a field experiment demonstrate the benefits of the proposed approach.