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Abstract
A method for parameter estimation is presented using the Kalman filter with appropriate initial conditions. The filter solution is shown to approximate the minimum-norm weighted least-squares solution to any desired accuracy during all phases of estimation. Furthermore, the computations are identical for each measurement, irrespective of whether a minimal observable data set has been established. This procedure contrasts with other techniques for parameter estimation that require additional computation when the process is unobservable.
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