## Lagrange Interpolation

Lagrange interpolation is a well known, classical technique for interpolation [193]. It is also called Waring-Lagrange interpolation, since Waring actually published it 16 years before Lagrange [309, p. 323]. More generically, the term*polynomial interpolation*normally refers to Lagrange interpolation. In the first-order case, it reduces to

*linear interpolation*.

Given a set of known samples , , the problem is to find the

*unique*order polynomial which

*interpolates*the samples.

^{5.2}The solution can be expressed as a linear combination of elementary th order polynomials:

where

*basis polynomial*for constructing a polynomial interpolation of order over the sample points . It is an order polynomial having zeros at all of the samples except the th, where it is 1. An example of a set of eight basis functions for randomly selected interpolation points is shown in Fig.4.10.

### Interpolation of Uniformly Spaced Samples

In the uniformly sampled case ( for some sampling interval ), a Lagrange interpolator can be viewed as a Finite Impulse Response (FIR) filter [449]. Such filters are often called*fractional delay filters*[267], since they are filters providing a non-integer time delay, in general. Let denote the

*impulse response*of such a fractional-delay filter. That is, assume the interpolation at point is given by

### Fractional Delay Filters

In*fractional-delay filtering*applications, the interpolator typically slides forward through time to produce a time series of interpolated values, thereby implementing a non-integer signal delay:

*delay line*in order to implement an

*interpolated delay line*(§4.1) that effectively provides a continuously variable delay for discrete-time signals. The

*frequency response*[449] of the fractional-delay FIR filter is

### Lagrange Interpolation Optimality

As derived in §4.2.14, Lagrange fractional-delay filters are*maximally flat*in the frequency domain at dc. That is,

*Butterworth filters*in classical analog filter design [343,449]. It can also be formulated in terms of ``Pade approximation'' [373,374]. To summarize, the basic idea of maximally flat filter design is to match exactly as many leading terms as possible in the Taylor series expansion of the desired frequency response. Equivalently, we zero the maximum number of leading terms in the Taylor expansion of the frequency-response error. Figure 4.11 compares Lagrange and optimal Chebyshev fractional-delay filter frequency responses. Optimality in the

*Chebyshev sense*means minimizing the worst-case error over a given frequency band (in this case, ). While Chebyshev optimality is often the most desirable choice, we do not have closed-form formulas for such solutions, so they must be laboriously pre-calculated, tabulated, and interpolated to produce variable-delay filtering [358].

### Explicit Lagrange Coefficient Formulas

Given a desired fractional delay of samples, the Lagrange fraction-delay impulse response can be written in closed form asThe following table gives specific examples for orders 1, 2, and 3:

*linear interpolation*,

*i.e.*, the interpolator impulse response is . Also, remember that, for order , the desired delay should be in a one-sample range centered about .

### Lagrange Interpolation Coefficient Symmetry

As shown in [502, §3.3.3], directly substituting into Eq.(4.7) derives the following*coefficient symmetry*property for the interpolation coefficients (impulse response) of a Lagrange fractional delay filter:

where is the order of the interpolator. Thus, the interpolation coefficients for delay are the ``flip'' (time reverse) of the coefficients for delay .

### Matlab Code for Lagrange Interpolation

A simple matlab function for computing the coefficients of a Lagrange fractional-delay FIR filter is as follows:function h = lagrange( N, delay ) n = 0:N; h = ones(1,N+1); for k = 0:N index = find(n ~= k); h(index) = h(index) * (delay-k)./ (n(index)-k); end

### Maxima Code for Lagrange Interpolation

The`maxima`program is free and open-source, like Octave for matlab:

^{5.3}

(%i1) lagrange(N, n) := product(if equal(k,n) then 1 else (D-k)/(n-k), k, 0, N); (%o1) lagrange(N, n) := product(if equal(k, n) then 1 D - k else -----, k, 0, N) n - kUsage examples in

`maxima`:

(%i2) lagrange(1,0); (%o2) 1 - D (%i3) lagrange(1,1); (%o3) D (%i4) lagrange(4,0); (1 - D) (D - 4) (D - 3) (D - 2) (%o4) - ------------------------------- 24 (%i5) ratsimp(lagrange(4,0)); 4 3 2 D - 10 D + 35 D - 50 D + 24 (%o5) ------------------------------ 24 (%i6) expand(lagrange(4,0)); 4 3 2 D 5 D 35 D 25 D (%o6) -- - ---- + ----- - ---- + 1 24 12 24 12 (%i7) expand(lagrange(4,0)), numer; 4 3 (%o7) 0.041666666666667 D - 0.41666666666667 D 2 + 1.458333333333333 D - 2.083333333333333 D + 1.0

### Faust Code for Lagrange Interpolation

The Faust programming language for signal processing [453,450] includes support for Lagrange fractional-delay filtering, up to order five, in the library file`filter.lib`. For example, the fourth-order case is listed below:

// fourth-order (quartic) case, delay d in [1.5,2.5] fdelay4(n,d,x) = delay(n,id,x) * fdm1*fdm2*fdm3*fdm4/24 + delay(n,id+1,x) * (0-fd*fdm2*fdm3*fdm4)/6 + delay(n,id+2,x) * fd*fdm1*fdm3*fdm4/4 + delay(n,id+3,x) * (0-fd*fdm1*fdm2*fdm4)/6 + delay(n,id+4,x) * fd*fdm1*fdm2*fdm3/24 with { o = 1.49999; dmo = d - o; // assumed nonnegative id = int(dmo); fd = o + frac(dmo); fdm1 = fd-1; fdm2 = fd-2; fdm3 = fd-3; fdm4 = fd-4; };An example calling program is shown in Fig.4.12.

// tlagrange.dsp - test Lagrange interpolation in Faust import("filter.lib"); N = 16; % Allocated delay-line length % Compare various orders: D = 5.4; process = 1-1' <: fdelay1(N,D), fdelay2(N,D), fdelay3(N,D), fdelay4(N,D), fdelay5(N,D); // To see results: // [in a shell]: // faust2octave tlagrange.dsp // [at the Octave command prompt]: // plot(db(fft(faustout,1024)(1:512,:))); // Alternate example for testing a range of 4th-order cases // (change name to "process" and rename "process" above): process2 = 1-1' <: fdelay4(N, 1.5), fdelay4(N, 1.6), fdelay4(N, 1.7), fdelay4(N, 1.8), fdelay4(N, 1.9), fdelay4(N, 2.0), fdelay4(N, 2.1), fdelay4(N, 2.2), fdelay4(N, 2.3), fdelay4(N, 2.4), fdelay4(N, 2.499), fdelay4(N, 2.5); |

### Lagrange Frequency Response Examples

The following examples were generated using Faust code similar to that in Fig.4.12 and the`faust2octave`command distributed with Faust.

#### Orders 1 to 5 on a fractional delay of 0.4 samples

Figure shows the amplitude responses of Lagrange interpolation, orders 1 through 5, for the case of implementing an interpolated delay line of length samples. In all cases the interpolator follows a delay line of appropriate length so that the interpolator coefficients operate over their central one-sample interval. Figure shows the corresponding phase delays. As discussed in §4.2.10, the amplitude response of every odd-order case is constrained to be zero at half the sampling rate when the delay is half-way between integers, which this example is near. As a result, the curves for the two even-order interpolators lie above the three odd-order interpolators at high frequencies in Fig.. It is also interesting to note that the 4th-order interpolator, while showing a wider ``pass band,'' exhibits more attenuation near half the sampling rate than the 2nd-order interpolator.#### Order 4 over a range of fractional delays

Figures 4.15 and 4.16 show amplitude response and phase delay, respectively, for 4th-order Lagrange interpolation evaluated over a range of requested delays from to samples in increments of samples. The amplitude response is ideal (flat at 0 dB for all frequencies) when the requested delay is samples (as it is for any integer delay), while there is maximum high-frequency attenuation when the fractional delay is half a sample. In general, the closer the requested delay is to an integer, the flatter the amplitude response of the Lagrange interpolator.#### Order 5 over a range of fractional delays

Figures 4.17 and 4.18 show amplitude response and phase delay, respectively, for 5th-order Lagrange interpolation, evaluated over a range of requested delays between and samples in steps of samples. Note that the vertical scale in Fig.4.17 spans dB while that in Fig.4.15 needed less than dB, again due to the constrained zero at half the sampling rate for odd-order interpolators at the half-sample point.###

Avoiding Discontinuities When Changing Delay

We have seen examples (*e.g.*, Figures 4.16 and 4.18) of the general fact that every Lagrange interpolator provides an integer delay at frequency , except when the interpolator gain is zero at . This is true for any interpolator implemented as a real FIR filter,

*i.e.*, as a linear combination of signal samples using real coefficients.

^{5.4}Therefore, to avoid a relatively large discontinuity in phase delay (at high frequencies) when varying the delay over time, the requested interpolation delay should stay within a half-sample range of some fixed integer, irrespective of interpolation order. This provides that the requested delay stays within the ``capture zone'' of a single integer at half the sampling rate. Of course, if the delay varies by more than one sample, there is no way to avoid the high-frequency discontinuity in the phase delay using Lagrange interpolation. Even-order Lagrange interpolators have an integer at the midpoint of their central one-sample range, so they spontaneously offer a one-sample variable delay free of high-frequency discontinuities. Odd-order Lagrange interpolators, on the other hand, must be shifted by sample in either direction in order to be centered about an integer delay. This can result in stability problems if the interpolator is used in a feedback loop, because the interpolation gain can exceed 1 at some frequency when venturing outside the central one-sample range (see §4.2.11 below). In summary, discontinuity-free interpolation ranges include

###

Lagrange Frequency Response Magnitude Bound

The amplitude response of fractional delay filters based on Lagrange
interpolation is observed to be bounded by 1 when the desired delay
lies within half a sample of the midpoint of the coefficient
span [502, p. 92], as was the case in all preceeding examples
above. Moreover, *even*-order interpolators are observed to have this boundedness property over a

*two*-sample range centered on the coefficient-span midpoint [502, §3.3.6]. These assertions are easily proved for orders 1 and 2. For higher orders, a general proof appears not to be known, and the conjecture is based on numerical examples. Unfortunately, it has been observed that the gain of some odd-order Lagrange interpolators do exceed 1 at some frequencies when used outside of their central one-sample range [502, §3.3.6].

### Even-Order Lagrange Interpolation Summary

We may summarize some characteristics of even-order Lagrange fractional delay filtering as follows:- Two-sample bounded-by-1 delay-range instead of only one-sample
- No gain zero at half the sampling rate for the middle delay
- No phase-delay discontinuity when crossing the middle delay
- Optimal (central) delay range is centered about an integer

### Odd-Order Lagrange Interpolation Summary

In contrast to even-order Lagrange interpolation, the odd-order case has the following properties (in fractional delay filtering applications):- Improved phase-delay accuracy at the expense of decreased amplitude-response accuracy (low-order examples in Fig.)
- Optimal (centered) delay range lies between two integers

###

Proof of Maximum Flatness at DC

The maximumally flat fractional-delay FIR filter is obtained by equating
to zero all leading terms in the Taylor (Maclaurin) expansion of
the frequency-response error at dc:
### Variable Filter Parametrizations

In 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 .

#### Table Look-Up

A general approach to variable 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 [462].#### Polynomials in the Delay

A more parametric approach is to formulate each 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 [134].

#### Farrow Structure

Taking the*z*transform of Eq.(4.9) yields

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 Coefficients

Beginning with a restatement of Eq.(4.9),where

and

Equation (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 Bank

Since, in the time domain, a Taylor series expansion of about time gives*ideal differentiator*, we see that the th filter in Eq.(4.10) should approach

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

Franck (2008) [148] provides a nice overview of the various structures being used for Lagrange interpolation, along with their computational complexities and depths (relevant to parallel processing). He moreover proposes a novel computation having linear complexity and log depth that is especially well suited for parallel processing architectures.### Relation of Lagrange to Sinc Interpolation

For an*infinite*number of

*equally spaced*samples, with spacing , the Lagrangian basis polynomials converge to shifts of the

*sinc function*,

*i.e.*, where

sinc

A simple argument is based on the fact that any analytic function is
determined by its zeros and its value at one point. Since
sinc
is zero on all the integers except 0, and since
sinc, it
must coincide with the infinite-order Lagrangian basis polynomial for
the sample at which also has its zeros on the nonzero integers
and equals at .
The equivalence of sinc interpolation to Lagrange interpolation was
apparently first published by the mathematician Borel in 1899, and has
been rediscovered many times since [309, p. 325].
A direct proof can be based on the equivalance between Lagrange
interpolation and windowed-sinc interpolation using a ``scaled
binomial window'' [262,502]. That is,
for a fractional sample delay of samples, multiply the
shifted-by-, sampled, sinc function
sinc

by a binomial window
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