Variable Filter ParametrizationsIn practical applications of Lagrange Fractional-Delay Filtering (LFDF), it is typically necessary to compute the FIR interpolation coefficients as a function of the desired delay , which is usually time varying. Thus, LFDF is a special case of FIR variable filtering in which the FIR coefficients must be time-varying functions of a single delay parameter .
filtering is to tabulate the filter coefficients as a function of the desired variables. In the case of fractional delay filters, the impulse response is tabulated as a function of delay , , , where is the interpolation-filter order. For each , may be sampled sufficiently densely so that linear interpolation will give a sufficiently accurate ``interpolated table look-up'' of for each and (continuous) . This method is commonly used in closely related problem of sampling-rate conversion .
filter coefficient as a polynomial in the desired delay :
Taking the z transform of this expression leads to the interesting and useful Farrow structure for variable FIR filters .
Since is an th-order FIR filter, at least one of the must be th order, so that we need . A typical choice is . Such a parametrization of a variable filter as a polynomial in fixed filters is called a Farrow structure [134,502]. When the polynomial Eq.(4.10) is evaluated using Horner's rule,5.5 the efficient structure of Fig.4.19 is obtained. Derivations of Farrow-structure coefficients for Lagrange fractional-delay filtering are introduced in [502, §3.3.7].
Farrow Structure CoefficientsBeginning with a restatement of Eq.(4.9),
andEquation (4.11) may be solved for the polynomial-coefficient matrix by usual least-squares methods. For example, in the unweighted case, with , we have
Differentiator Filter BankSince, in the time domain, a Taylor series expansion of about time gives
in the limit, as the number of terms goes to infinity. In other terms, the coefficient of in the polynomial expansion Eq.(4.10) must become proportional to the th-order differentiator as the polynomial order increases. For any finite , we expect to be close to some scaling of the th-order differentiator. Choosing as in Eq.(4.12) for finite gives a truncated Taylor series approximation of the ideal delay operator in the time domain [184, p. 1748]. Such an approximation is ``maximally smooth'' in the time domain, in the sense that the first derivatives of the interpolation error are zero at .5.6 The approximation error in the time domain can be said to be maximally flat. Farrow structures such as Fig.4.19 may be used to implement any one-parameter filter variation in terms of several constant filters. The same basic idea of polynomial expansion has been applied also to time-varying filters ( ).
Recent Developments in Lagrange Interpolation
Proof of Maximum Flatness at DC