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Finally, set \(y = [y1, y2]\). (a) Show (by reducing this to a problem that we already know how to solve - don't start from scratch!) (Hint: One approach to solving this is to use our recursive least squares formulation, but modified for the limiting case where one of the measurement sets - namely \(z = Dx\) in this case - is known to have no error. While recursive least squares update the estimate of a static parameter, Kalman filter is able to update and estimate of an evolving state. Even though your estimation algorithms will assume that \(a\) and \(b\) are constant, we are interested in seeing how they track parameter changes as well. It does this by solving for the radial} \\ version (4.88 KB) by Ryan Fuller. Aliases. Legal. Use \(f = .96\), (iii) The algorithm in (ii), but with \(Q_{k}\) of Problem 3 replaced by \(q_{k} = (1/n) \times trace(Q_{k})\), where \(n\) is the number of parameters, so \(n = 2\) in this case. 1 m i=1 y i~a i I recursive estimation: ~a i and y i become available sequentially, i.e., m increases with time 23 Downloads. that the value \(\widehat{x}_{k}\) of \(x\) that minimizes the criterion, \[\sum_{i=1}^{k} f^{k-i} e_{i}^{2}, \quad \text { some fixed } f, \quad 0

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