# Alias-free digital nonlinear filter design

@Jazzmaniac has a good answer to the question of how to design an alias-free digital nonlinear time-invariant filter here: https://dsp.stackexchange.com/a/28787/18276

Basically, according to that answer, a digital nonlinear time-invariant filter is alias free if and only if it commutes with subsample translations. Meaning that it doesn't matter whether you filter and then translate, or translate and then filter. Sinc interpolation is required for perfect subsample translations, but of course you can always use a finite interpolator that is good enough.

This question is to elaborate:

1. How can we see the link between subsample translation invariance and aliasing?

2. Is there any easy way to see what these filters look like?

3. Do the filters have some standard form they can be put in?

4. Do we know what the alias-free version of the monomials look like? (i.e. the alias-free version of $y[t]=x[t]^n$ for some positive natural number $n$)

5. Are there any good references or published works on the topic of alias-free nonlinear filter design?

• what does it mean to "commutes with subsample translations"? Aug 27 '18 at 3:45
• Edited the post to clarify Aug 27 '18 at 16:17
• okay, i still cannot grok what you mean precisely by a "nonlinear filter" and do not agree that what appears to me to be the property of time-invariance (the "TI" of "LTI") is related to aliasing. Aug 27 '18 at 16:37
• Digital nonlinear filters can, in general, cause aliasing - even simple instantaneous ones like $y[t]=x[t]^2$. I edited the post to clarify this is about digital filters only. You can look at the original thread linked for additional context. Note that subsample time invariance is a much stronger criterion than ordinary whole-sample time invariance. The link between aliasing and subsample time invariance was claimed to be there by Jazzmaniac in the other thread, and when I asked him to elaborate he asked if I could make a new question for it, and here we are. Aug 27 '18 at 17:16
• For that filter to be alias-free, you need to upsample by 2x. Aug 27 '18 at 22:34

First, allow me to address the subsample shift property in relation to non-linear signal mappings. It is fairly straightforward to see time shifting is not as simple a property as for linear systems. Consider a discrete time signal given by the sequence $$\dots ,1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, \dots$$ This sequence is invariant under memoryless nonlinearities of the form $x\mapsto x^n$ for natural $n$. If the above sequence is shifted by a fraction of the sampling interval, the sample values will not be $0$ and $1$ anymore, and the resulting sequence will lose its invariance under the nonlinear map given.

Enforcing this invariance and allowing only nonlinear maps that preserve it is equivalent to removing aliasing, as I will be showing here when I find a little time to expand upon my answer.

Edit: Some more details.

For simplicity, we will look at a discrete time signal centred around $t=0$ with a sampling interval of $T=1$. The discrete signal can be expanded using a power series $$x(t) = \sum_{n=0}^{2N} D_{N}^{(n)} \frac{t^n}{n!}$$ which describes $x[t]$ in the interval $t\in [-N,N]$. The coefficients $D_{N}^{(n)}$ are chosen so that the polynomial is the minimal interpolating polynomial on this interval. They coincide with the $n$-th discrete derivatives of $x[t]$ at $t=0$, also on this interval. The interpolating nature of this expansion makes it a natural linear homomorphism to the space of continuous time signals. It is worth noting, that in the limiting case $N\to\infty$, the interpolation approaches the bandlimited interpolation of the $\mathrm{sinc}$ kernel.

Applying a differential time shift $dt$ to a smooth continuous time signal $x(t)$ can be written as $$x(t-dt)=x(t) - dt \frac{\partial}{\partial t}x(t)=(1-dt \frac{\partial}{\partial t})x(t)$$ For finite shifts $\delta t$, we can concatenate many smaller shifts and take the limit $$x(t-\delta t)=\lim_{n\to\infty}\left(1-\frac{\delta t}{n}\frac{\partial}{\partial t}\right)^n \,x(t)=\exp\left(-\delta t \frac{\partial}{\partial t}\right)\,x(t)$$ Therefore the linear operator $S(\delta t)=\exp\left(-\delta t\frac{\partial}{\partial t}\right)$ shifts smooth continuous time signals.

The interpolation polynomial above is also an expansion of the $\exp$ function into a power series, with the discrete derivative taking the place of the partial derivative of the shift operator. For finite orders of the interpolation polynomial, the continuous time shift operator therefore approximates shifts of the interpolating function. This approximation becomes exact in the limit of infinite order.

With this understanding, we can calculate an (approximately) alias free memoryless nonlinearity $f$ acting on $x[t]$. We only need to evaluate $f(x[0])$, all other times follow by integer sample shifts.

With the interpolated continuous-time signal $x(t)$ approximating a band-limited reconstruction of $x[t]$, we can apply the nonlinearity in continuous time and then band-limit the result using an approximation of a band-limiting kernel $b(t)$ to avoid aliasing. A sufficient condition for a feasible symbolic evaluation of the band-limited result is that $f$ and $b$ be polynomials. Then $f(x(t))$ is a polynomial and we can directly calculate

$$y(0) = \int_{-N}^{N} f\left(x(t)\right) b(t) dt$$

which, in the limit of large orders $N$, achieves both full translation invariance and perfect alias rejection. This is not a proof that both are equivalent, but a good starting point to understand how these two properties are linked.

Suitable choices for $b$ include polynomial expansions of the $\mathrm{sinc}$ function. For example

$$b_N(t)=\frac{(N^2-t^2) \prod_{n=1}^{2N}(t^2-n^2) }{N^2 \prod_{n=1}^{2N}n^2}$$

for an approximately bandlimited kernel on the interval $t \in [-N,N]$.

This much must suffice as theoretical motivation for now.

Example

The simplest, non-trivial example is that with the minimal neighbourhood involvement and a crude approximation to a band-limited kernel. Do not expect good anti-aliasing properties from it. It's only here to demonstrate the general procedure of creating an anti-aliased memoryless nonlinearity.

We use the lowest possible order, $N=1$, and arrive at the expansion

$$x(t) = D_1^{(0)} t^0 + D_1^{(1)} t^1 + D_1^{(2)} \frac{t^2}{2}$$

where

$$D_1^{(0)} = x[0]\\ D_1^{(1)}=\frac{1}{2}(x[1] - x[-1])\\ D_1^{(2)}=x[1]-2x[0]+x[-1]$$

and as a single expression

$$x(t)= x[0] + \frac{1}{2}(x[1]-x[-1])\, t + \frac{1}{2}(x[1]-2x[0]+x[-1])\,t^2$$

The nonlinearity is assumed to be a monomial $x\mapsto x^k$ and we take the $b_1$ from above and get

$$y_k[0] = \int_{-1}^{+1} \left(x[0] + \frac{1}{2}(x[1]-x[-1])\, t + \frac{1}{2}(x[1]-2x[0]+x[-1])\,t^2\right)^k \, \cdot \frac{1}{4}(4-t^2)(t^2-1)^2 dt$$

We can evaluate this expression for $k=2$ and also remove the $t=0$ simplification made earlier to arrive at a nonlinear filter

$$y_2[n] = \frac{4}{3465}(688 x[n]^2 + 40 x[n-1]^2 + 40 x[n+1]^2 - 41 x[n-1] x[n+1] + 82 x[n](x[n+1]+x[n-1]))$$

Generalising for non-smooth nonlinearities

The argument so far has required that the non-linearity can be well approximated by a polynomial. If that is not the case, then the integral will generally be harder to evaluate and the existence of a closed form solution is not even guaranteed. This is where the equivalence of commutativity of the filter with sub-sample shifts comes in.

The most general form of a nonlinear filter on a neighborhood $[-N,N]$ around $t=0$ is that of a nonlinear map

$$y[0] = F( x[-N],x[-N+1],\dots,x[N-1],x[N] )$$

For a memoryless non-linearity, we want to map constant input signals to constant output signals, according to the non-linear transfer function $f$. If the constant input is $X$, then we have the condition

$$F(X,X,\dots,X) = f(X)$$

and from the shift-invariance we have the condition

$$F( S(\delta t) x[-N], S(\delta t) x[-N+1],\dots, S(\delta t)x[N-1],S(\delta t) x [N]) = S(\delta t)F( x[-N],x[-N+1],\dots,x[N-1],x[N] )$$ for all $\delta t \in \mathbb{R}$

In general, there is no symbolic solution for this problem. It is however approachable with numerical optimization methods. In many cases, the free parameters can be reduced further by restricting the form of $F$.

I believe I should have answered all your questions with exception of the request for literature pointers. I am not aware of any. I don't know if this theory has ever been presented. But I believe the idea is not too difficult to come up with, so it has probably been done before. If you find something, please do let me know.

• Comments are not for extended discussion; this conversation has been moved to chat.
– Peter K.
Sep 1 '18 at 22:11
• "This interval" meaning $t \in [-N,N],$ "$n$-th discrete derivative of $x[t]$ at $t=0,$ also on this interval" means the $n$-th derivative of the Lagrange polynomial through points $(t, x[t]),$ with $t \in \{-N, -N+1, \ldots, N-1, N\},$ right? Sep 7 '18 at 13:02
• @OlliNiemitalo, yes, from the uniqueness of the minimal interpolation polynomial, it follows that the expansion can be written in terms of the Lagrange polynomial basis. A Taylor expansion of that form around $0$ shows that the $n$-th derivatives of the sample-weighted sum of the Lagrange basis is identical to the $n$-th discrete derivative as used in my expansion. Sep 7 '18 at 14:53

There are many disparate (and, for me, sometimes confusing) things said here. I believe some of this is because of semantic differences. That said:

1. I normally use the term "filter" for LTI processes. I use the term terms "nonlinear process" or "nonlinear system" for something that is not L (but may be and usually is TI).
2. Systems that are both nonlinear and time-invariant (not L but yes on TI) and having memory can be most-generally expressed using Volterra series, but there are so many terms with non-zero coefficients that I have never really tried to do anything (like model a vacuum tube circuit with capacitors and transformers and such) with it.
3. The only times I ever did anything that was nonlinear was deliberately separate the nonlinear parts from the parts with memory and model them separately. So the components are either linear with memory and LTI system theory applies (convolution, Fourier Transform, etc.) or nonlinear without memory.
4. Modeling a memoryless nonlinear component within a context of LTI components connected to it can be done if the nonlinear component can be accurately described with a finite-order polynomial with order of, say, $K$. If a sinusoid of angular frequency $\omega_0$ is input to this $K$th order polynomial, then the output will be a periodic function with fundamental at $\omega_0$ and overtones having frequencies of $k \omega_0$ where $k$ is an integer such that $2 \le k \le K$.
5. So the highest frequency is $K \omega_0$. If $\omega_0<\frac{\pi f_\mathrm{s}}{K}$, then all frequencies will be below Nyquist and no aliasing occurs.
6. So oversampling by a factor of $K$ (same as the order of the polynomial) is sufficient to prevent aliasing. But if the idea is to upsample by a factor of $K$, do the nonlinearity, then low-pass filter with cutoff at $\frac{\pi}{K}$ ($\pi =$ Nyquist), then downsample by a factor of $\frac{1}{K}$ (decimate by tossing $K-1$ samples out of every $K$ samples), then it turns out that you can allow some aliasing to occur as long as the aliases don't fold over so far as to get into your original baseband.
7. If you consider that, you need only upsample by a factor of $\frac{K+1}{2}$ to avoid aliasing when using a memoryless nonlinearity modeled as a polynomial of order $K$. Upsampling by a factor of $4$ suffices to implement a polynomial of order $7$, if everything in the top $\frac{3}{4}$ of the upsampled spectrum is eliminated (with an LPF, leaving only the bottom $\frac{1}{4}$ of the upsampled spectrum) and downsampling by a factor of $\frac{1}{4}$ (returning to the original sample rate) is allowed and none of the aliases of that $7$th-order polynomial nonlinearity will make it into your final spectrum.
• Comments are not for extended discussion; this conversation has been moved to chat.
– Peter K.
Sep 1 '18 at 22:10