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AUTHOR(S): Nicholas Assimakis, Maria Adam
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ABSTRACT In this paper we propose metrics for the reliability of Kalman filter prediction and estimation. These metrics depend on the known state and measurements noise covariances, the prediction or estimation error covariances and the measurements covariance. These metrics concern the time varying, the time invariant and the steady state Kalman filters. The proposed metrics are time varying since the measurements covariance is time varying. The closer the metrics are to zero, the better the prediction or the estimation. Examples are presented to test the proposed metrics. |
KEYWORDS Kalman filter, Estimation, Prediction, Steady state, Metrics, State Space Systems, Measurement |
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Cite this paper Nicholas Assimakis, Maria Adam. (2026) Reliable Kalman Filter Prediction and Estimation Metrics. International Journal of Mathematical and Computational Methods, 11, 39-45 |
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