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So it recently dawned on me that Bessel filters, despite being listed along with the other common types, are really an oddball that belongs in a different "class", and I'm trying to learn more about it.

The rectangular magnitude response represents the ideal frequency domain response, for the transition band is zero and the stopband has infinite attenuation. The Gaussian magnitude response, on the other hand, represents the ideal time-domain response, in that no overshoots occur in the impulse response and the step response. Many of the responses attained in practice are approximations to these ideal ones source

So a brickwall filter is convolution with a sinc function, and has these frequency domain properties:

  • Flat passband
  • Zero stopband
  • Infinite roll-off rate/no transition band

It's non-causal and unrealizable because of the infinite tails in both directions. It is approximated by these IIR filters, with the approximation improving as order increases:

  • Butterworth (maximally flat passband)
  • Chebyshev (maximum roll-off rate with stopband or passband ripple)
  • Elliptic (maximum roll-off rate with stopband and passband ripple)
  • Legendre (maximum roll-off rate with monotonic passband)

4 filter types approaching brickwall response as order increases


The Gaussian filter is convolution with a Gaussian function, and has these time domain properties:

  • Zero overshoot
  • Minimal rise and fall time
  • Minimal group delay

It's unrealizable for the same reasons as the sinc function, and can be approximated by these IIR filters, more closely as order increases:

  • Bessel (maximally flat group delay) according to 1 and 2

Here's Bessel filters of increasing order along with a Gaussian dashed line I picked merely because it seemed to fit the trend ($e^{-{1 \over 2}(\pi \omega)^2}$):

Bessel filters supposedly approaching Gaussian response as order increases, with guess for Gaussian

So my questions are:

Is everything right so far? If so, are there other IIR filters that approximate the Gaussian? What are they optimized for? Maybe one that minimizes overshoot?

If you search for "IIR Gaussian" you can find a few things (Deriche? van Vliet?), but I don't know if they're really the same as a Bessel or if they optimize for some other property, etc.

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  • $\begingroup$ actually, when I say "IIR" I think I really mean "physically realizable analog filters"? $\endgroup$ – endolith Jun 28 '13 at 3:55
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The Deriche and van Vliet filters are heuristics. In both cases they choose the locations of poles and zeros to minimize either the RMS difference or the maximum difference of the filter's impulse response from a Gaussian.

Both filters are causal-anti-causal pairs. So I think they have no phase error or group delay, but you need to be able to run them backwards on the data as well as forwards. This makes them popular in image processing, but perhaps restricts their applicability elsewhere.

That they are heuristics is evidenced by the wealth of papers that tweak them. For example a google search (while I was looking for the link to the Deriche paper) turned up this one that tries to solve the problem that the Deriche derivative-of-gaussian filter doesn't have an exactly 0 DC response. There are also some interesting issues about initializing the boundary conditions correctly.

I have found the following overview a good resource: Dave Hale, Recursive Gaussian filters, Colorado School of Mines Center for Wave Phenomena report CWP-546.

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I think you gave a nice summary of existing analytic solutions for discrete-time IIR filters. But I would also add Bessel filters to the list of filters approximating ideal frequency-selective filter characteristics. Its magnitude response does not show as sharp a transition as the other filter types of the same order, but this is the price you pay for an almost linear phase in the passband. So the Bessel filter is a compromise between a frequency-selective magnitude response and a good phase response.

For approximating a Gaussian filter with IIR filters, I do not know of any analytic solutions, apart from the Bessel filter you mentioned. But note that the Bessel filter was not meant to approximate a Gaussian filter, so I'm not sure how good it really is in approximating such a filter. If you really want an IIR filter for this purpose, I would suggest you go for numerical approximation of the Gaussian filter. There are several options how to do this.

You could try to approximate the Gaussian filter in the frequency domain. The problem is that you have to make some decision concerning the desired phase response. A pure magnitude approximation with minimum phase response will very likely result in very poor time-domain properties. If you specify a linear desired phase, then you get a complex approximation problem (because you approximate the complex frequency response with magnitude and phase). Even though such an approximation problem can be quite tough to solve, there exist methods in the literature.

A simpler and probably better approach is to approximate the Gaussian filter in the time domain. Prony's method would be a good starting point.

Please note that these are just my thoughts on the topic. I have not tried to design an IIR Gaussian filter myself. I would actually go for an FIR implementation unless there are very good reasons against it.

EDIT: just a few more remarks concerning the question whether a Bessel filter approximates a Gaussian or not. I do not know of any meaningful error criterion which the Bessel filter minimizes in approximating a Gaussian filter. I would be glad to learn about it though. People may claim that the impulse response of a Bessel filter looks similar to Gaussian, or that its frequency response resembles a Gaussian, but I have not yet seen any proof that Bessel filters approximate a Gaussian in any sense, and that the approximation error goes to zero as the filter order increases. I do not deny that it is more similar to a Gaussian than the other standard filters (Butterworth, Chebyshev, etc.), but this is not important for the question.

See below four plots of impulse responses of Bessel filters (orders 5, 10, 15, 20), designed in Octave (function besself). As you can see, the ringing in the tail does not decrease with increasing filter order, and I do not see how these filters approximate a Gaussian, and if so, according to which optimality criterion. However, if anybody can enlighten me about this, I would be more than happy.

enter image description here

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  • $\begingroup$ I'm getting it from places like these: "The impulse response of Bessel-Thomson filters tends towards a Gaussian as the filter order is increased" robots.ox.ac.uk/~sjrob/Teaching/SP/l3.pdf "An analog Bessel filter is an approximation to a Gaussian filter, and the approximation improves as the filter order increases." dsprelated.com/showmessage/130958/1.php $\endgroup$ – endolith Jun 26 '13 at 21:38
  • $\begingroup$ ...and since the Fourier transform of a Gaussian is a Gaussian, I don't think it's right to say that it approaches a brickwall response like the others do. $\endgroup$ – endolith Jun 26 '13 at 21:54
  • $\begingroup$ The Fourier transform of a Gaussian is a Gaussian, no doubt about that. But we're talking about Bessel filters, which - as far as I know - do not approximate a Gaussian in any meaningful way. I edited my answer to add more information. $\endgroup$ – Matt L. Jun 27 '13 at 13:11
  • $\begingroup$ Added some more details to the question about this. Can you redo your plots as line curves instead of stems, and with the same Y axis? It looks like it's changing shape with order but it's hard to tell. $\endgroup$ – endolith Jun 28 '13 at 4:06
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    $\begingroup$ I tried calculating the bessel filters up to order 60 and fitting their impulses responses by gaussians, and although my code is crude, they do seem to approach the gaussian, with "undershoot" decreasing and error decreasing with order. 10th order: imgur.com/1qNsHeg 60th order: imgur.com/BgmFzZp error as order increases: imgur.com/cpHDDJs the calculations might be wrong, though. how did you calculate yours? $\endgroup$ – endolith Oct 12 '15 at 3:15

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