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Which consequence results when a Kalman filter's process noise covariance function incorrectly estimates the true system dynamics?

A)Decreased computational resource utilization
B)Optimal input signal convergence
C)Suboptimal state variable estimation
D)Improved observer disturbance rejection

💡 Explanation

Suboptimal state estimation occurs because inaccurate process noise causes the Kalman filter to trust its internal model too much, rather than measurements. Therefore, it relies excessively on flawed equations, rather than observations, leading to less accurate estimations.

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