Cost function Covariance

The idea of selecting waveforms adaptively based on tracking considerations was introduced in the papers of Kershaw and Evans [3, 4], There they used a cost function based on the predicted track error covariance matrix. 

We have, on the other hand done simple simulations for the case of one-step ahead and two-step ahead scheduling. In the latter case, the revisit times and waveforms are calculated while the target states are propagated forward over two measurements, with the cost function being the absolute value of the determinant of the track error covariance after the second measurement. Only the first of these measurements is done before the revisit calculation is done again for that target, so that the second may never be implemented. 

The essential step in the LQG benchmark is the calculation of various control laws for different values of A and prediction (P) and control (M) horizons (P = M). This is a case study for a special type of MPC (unconstrained, no feedforward) and a special parameter set (M = P) to find the optimal value of the cost function and an optimal controller parameter set. Using the same information (plant and disturbance model, covariance matrices of noise and disturbances), studies can be conducted for any t3q>e of MPC and the influence of any parameter can be examined. These studies... 

The infinite uncertainty gives a zero contribution to the cost function minimized by the adjustment procedure [2, App. E]. A finite uncertainty in equation (5) would essentially not have changed this situation the principle behind our predictions of new energies consists in using all available information that constrains the adjusted variables Zu all such information is already included in the extended adjustment [1] in the form of the equations of CODATA 2002 [3] (described in Section 2.2), and in the form of covariances involving the new 5 s... 

Regression analysis often is used to assess differences in costs, in part because the sample size needed to detect economic differences may be larger than the sample needed to detect clinical differences (i.e., to overcome power problems). Traditionally, ordinary least-squares regression has been used to predict costs (or their log) as a function of the treatment group while controlling for covariables such as... 

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