Which outcome results when an unoptimized Kalman filter overestimates noise?

💡 Explanation

State estimates become overly sensitive because the Kalman filter places more weight on measurements than state transition models; therefore errors propagate more easily, rather than being damped quickly because the algorithm trusts unreliable recent measurement data.

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