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while studying the sign-error LMS algorithm. This particular assumption was shown to be valid asymptotically in Sharma, Sethares, and Bucklew (1996). More accurate is the assumption that the residual error is Gaussian as in Duttweiler (1982) and Bershad and Bonnet (1990). or that its conditional value is Gaussian as in Mathews and Cho (1987) and Bershad (1988). Although it has been argued in Masry and Bullo (1995) that the Gaussian assumption on e , ( i ) for the sign-error LMS algorithm does not exactly hold under the independence assumption, still the Gaussianity assumption on e.(i) is reasonable for long filters by central limit arguments. 4. Restricting the class of input data, such as assuming white andor Gaussian regression data as done, for example, in Claasen and Mecklenbrauker (1981), Duttweiler (1982), Gardner (1984), Gibson and Gray (1988), Rupp (1993), and Douglas and Meng (1994a). 5. Using the independence assumptions, whereby the successive regressors are assumed to be independent of each other as in Mazo (1979). Despite being unrealistic, the independence assumptions are among the most heavily used assumptions in adaptive filtering analysis and they tend to lead to results that agree with practice for small step-sizes.

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0.7113 = ~ = 1.516. 0.4693

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If the reading beside the numbers says something other than in (inches), then your ruler preferences are set to something other than inches. This is easy to fix. If the ruler isn t currently visible, choose View Ruler (Command/Ctrl+R), right-click the ruler, and select Inches. (I like to leave the Ruler visible because it s useful for other things too.)

These interpretationswere used in Sec. 42.1and 43 to derive different forms of lattice filters by solving first-order least-squares problems. we In this problem, as well as in Robs. X.12-X.13, shall derive the same filters by appealing instead to the equivalence relation that exists between RLS and Kalman filtering. Thus recall from the discussion in App. 31.2 that we showed that the exponentially-weighted RLS algorithm can be obtained via equivalence to the Kalman filter as follows. We start from the state-space model (31.8) write down the Kalman recursions for estimating its state variable. Then we translate the and In Kalman variables into the RLS variables by using the correspondencessummarized in Table 3 1.2. Robs. X.11-X.13,we want to use this equivalence construction in order to re-derive lattice filters. As a result, we shall clarify the connections between lattice filters and Kalman filtering. Use (a) Let us first consider the problem of projecting f&,ionto 6&,* in order to evaluate &(i). . the equivalence between RLS and Kalman filtering from App. 3 12 to argue that this problem

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