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Introduction and Implementations of the Kalman Filter [Working Title]
For designing an optimal Kalman filter, it is necessary to specify the statistics, namely the initial state, its covariance and the process and measurement noise covariances. These can be chosen by minimising some suitable cost function J. This has been very difficult till recently when a near optimal Recurrence Reference Recipe (RRR) was proposed without any optimisation but only filtering. In many filter applications after the initial transients, the gain matrix K tends to a constant duringdoi:10.5772/intechopen.81795 fatcat:5snlj7ys6jcori56ukkhtuifdm