The Composite Covariance of a Kalman Filter Estimation

Winston C Chow

Abstract


A Kalman filter estimation of the state of a system is merely a random vector that has a normal, also called Gaussian, distribution. Elementary statistics teaches any Gaussian distribution is completely and uniquely characterized by its mean and covariance (variance if univariate). Such characterization is required for statistical inference problems on a Gaussian random vector. This mean and composite covariance of a Kalman filter estimate of a system state will be derived here. The derived covariance is in recursive form. One must not confuse it with the “error covariance” output of a Kalman filter. Potential applications, including geological ones, of the derivation are described and illustrated with a simple example.

Keywords


Statistical inference, Kalman filter, composite covariance, mean, estimation, Gaussian, process noise, measurement noise, probabilistic distribution, prediction, unbiased

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DOI: https://doi.org/10.15273/ijge.2021.06.083

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