4.6.a Impulse truncation and Gibbs phenomenon

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Digital Signal Processing

376 calificaciones

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École Polytechnique Fédérale de Lausanne

Digital Signal Processing

376 calificaciones

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. The goal, for students of this course, will be to learn the fundamentals of Digital Signal Processing from the ground up. Starting from the basic definition of a discrete-time signal, we will work our way through Fourier analysis, filter design, sampling, interpolation and quantization to build a DSP toolset complete enough to analyze a practical communication system in detail. Hands-on examples and demonstration will be routinely used to close the gap between theory and practice. To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course.

De la lección

Module 4: Part 2 Filter Design

4.6.a Impulse truncation and Gibbs phenomenon3:48

4.6.b Window method10:36

4.6.c Frequency sampling2:51

Signal of the Day: Resolution and Space Exploration19:21

Conoce a los instructores

Paolo Prandoni
Lecturer
School of Computer and Communication Science
Martin Vetterli
Professor
School of Computer and Communication Sciences

So you remember an ideal filter is not realizable in practice because the impulse

response is a two-sided infinite support sequence.

So we're going to try to approximate it by starting from the ideal impulse response

and by manipulating this response into something that we can actually deal with.

In the next examples, we will concentrate on the design of a low pass filter, but

certainly, the same techniques can be applied to any type of ideal filter.

So the first idea is the following.

We pick a cut off frequency, omega c.

We compute the ideal impulse response for the lowpass, analytically.

We truncate h[n] to a finite support, hat h[n].

This hat h[n] defines an FIR filter that we can use.

For the time being, we don't worry about causality, so we preserve the symmetry

around zero of the impulse response and we approximate the ideal filter

with an FIR of length to 2N + 1 center around zero.

And so, here hat h[n] is equal to omega c over Pi times the sinc of omega c over Pi

for n less or equal to capital N in magnitude and zero everywhere else.

A justification of this method, is the fact that if we compute the mean square

error between the original filter and the approximation.

We have that the norm of the error int he frequency domain,

is the integral from minus pi to pi of the frequency response of the ideal

filter minus the frequency response of the approximated filter squared.

This equal to the norm squared of the difference between the two frequency

responses.

And thanks to Parseval's theorem, this is also the norm squared

of the difference between the impulse responses in the time domain.

And so we can write that, the mean square error,

as simply the sum from n that goes to minus infinity to plus infinity

of the difference between the two impulse responses squared.

The impulse response of the ideal low pass is a sink and

you remember, it decays monotomically as 1/n.

So the mean square error is clearly minimized if we pick values for

hat h[n] from an interval that is symmetric around zero,

because in this way we're actually killing in that summation the largest terms.

So we have seen why impulse truncation might be a good idea but

it's time to look at why it might not be such a good idea after all.

And to appreciate that,

we have to look at the frequency response of the approximate filter.

So here in this block, we show the frequency response of an ideal low-pass

filter, where we choose omega c equal to pi over 2.

And superimpose we're going to show the frequency response of the approximate

filter for increasing values of capital M.

So we start with M = 9 which is not really a very long filter and

we see the approximation is not particularly good.

So we increase the number of downs of the filter.

And as we add more and more points to the impulse response,

we see that the characteristic in red approaches the characteristic in green.

But it looks as though the maximum error near the transition point for

the frequency response never really goes down

in spite of the number of points that we used for the approximation.

We can try and push Number of points even higher.

And we see that even for 300 points, the maximum error around

omega c is always of the same amplitude.

We can prove mathematically that the maximum error around the cutoff frequency

is about 9% of the height of the jump, regardless of the number of points.

This is a standard result in approximation theory known under the name of

Gibbs phenomenon.

It always happens when we try to approximate a discontinues function.

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