Online Stochastic Convergence Analysis of the Kalman Filter
نویسندگان
چکیده
This paper presentsmodifications to the stochastic stability lemmawhich is thenused to estimate the convergence rate andpersistent error of the linear Kalman filter online without using knowledge of the true state. Unlike previous uses of the stochastic stability lemma for stability proof, this new convergence analysis technique considers time-varying parameters, which can be calculated online in real-time tomonitor the performance of the filter.Through simulation of an example problem, the newmethodwas shown to be effective in determining a bound on the estimation error that closely follows the actual estimation error. Different cases of assumed process and measurement noise covariance matrices were considered in order to study their effects on the convergence and persistent error of the Kalman filter.
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