Bayesian Adaptive Kalman Filtering by Separable Approximation of Posterior Distribution

We present a new method for Bayesian adaptive Kalman filtering, which is based on separable approximation of posterior distribution of states and parameters. Measurement noise adaptive Kalman filter and extended Kalman filter are derived as special cases of the method, and using simulation results we demon- strate their performance in practical applications.

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