Nonlinear Kalman filters systematically underestimate posterior covariance under nonlinear measurements; a post-update recalibration framework corrects this overconfidence and can be added to existing filters.
Convergence analysis of nonlinear kalman filters with novel innovation-based method
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Mitigating Overconfidence in Nonlinear Kalman Filters via Covariance Recalibration
Nonlinear Kalman filters systematically underestimate posterior covariance under nonlinear measurements; a post-update recalibration framework corrects this overconfidence and can be added to existing filters.