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The optimal gain matrix of the Kalman filter is often derived by minimizingthe trace of the posterior covariance matrix. Here, I show that the Kalman gainalso minimizes the determinant of the covariance matrix, a quantity known asthe generalized variance. When the error distributions are Gaussian, thedifferential entropy is also minimized.

Proof that the Kalman gain minimizes the generalized variance