A variational Bayesian adaptive Kalman filter with dual Bernoulli masks jointly estimates states and unknown noise covariances under intermittent corrupted observations, converging to optimal bounds as sensor count increases.
The fine calibration of the ultra-short baseline system with inaccurate measure- ment noise covariance matrix
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State estimations and noise identifications with intermittent corrupted observations via Bayesian variational inference
A variational Bayesian adaptive Kalman filter with dual Bernoulli masks jointly estimates states and unknown noise covariances under intermittent corrupted observations, converging to optimal bounds as sensor count increases.