arXiv:2609.14271v2 Announce Type: replace Abstract: Kalman filters (KFs) choose the gain as the product of the state-measurement cross-covariance and the inverse of the innovation covariance. In nonlinear systems, the predictive distribution generally changes shape, and these covariances require approximation. Conventional nonlinear KFs approximate them around a single center, the predicted state. They can produce inaccurate gains and overconfident covariance estimates when one local approximation does not represent the measurement geometry across the uncertainty region. To address this issue, we introduce probe sets, small covariance-scaled collections of states at which the filter repeats its local approximation. We then select the gain that minimizes the average covariance reported by these local approximations. The same construction is used in our previously proposed covariance recalibration step to improve covariance consistency. The construction applies to different KF variants without requiring a specific approximation rule. For quadratic measurements, we derive conditions under which probing yields lower normalized true error variance than the corresponding single-center gain when prediction uncertainty is sufficiently understated. We apply the framework to four KF variants and compare the resulting filters with multiple baselines across 300 randomized setups for each of two systems. The results show that probing preserves typical accuracy while substantially reducing covariance inconsistency and the rare large-error trajectories that dominate the root mean square of per-run errors. The code is available at https://github.com/Shida-Jiang/Probe_KF.