Lecture 4 frequency domain representation dtft idtft dft idft
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Lecture 4: Frequency domain representation, DTFT, IDTFT, DFT, IDFT. Instructor: Dr. Gleb V. Tcheslavski Contact: [email protected] Office Hours: Room 2030 Class web site: http://ee.lamar.edu/gleb/dsp/index.htm. Some history.

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Lecture 4: Frequency domain representation, DTFT, IDTFT, DFT, IDFT

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Lecture 4 frequency domain representation dtft idtft dft idft

Lecture 4: Frequency domain representation, DTFT, IDTFT, DFT, IDFT

Instructor: Dr. Gleb V. Tcheslavski

Contact:[email protected]

Office Hours: Room 2030

Class web site:http://ee.lamar.edu/gleb/dsp/index.htm


Some history

Some history

Jean Baptiste Joseph Fourier was born in France in 1768. He attended the Ecole Royale Militaire and in 1790 became a teacher there. Fourier continued his studies at the Ecole Normale in Paris, having as his teachers Lagrange, Laplace, and Monge. Later on, he, together with Monge and Malus, joined Napoleon as scientific advisors to his expedition to Egypt where Fourier established the Cairo Institute.

In 1822 Fourier has published his most famous work: The Analytical Theory of Heat. Fourier showed how the conduction of heat in solid bodies may be analyzed in terms of infinite mathematical series now called by his name, the Fourier series.


Frequency domain representation

Frequency domain representation

(4.3.1)

frequency

(4.3.2)

complex exponent of 0

complex exponent of -0

A sinusoidal signal is represented by TWO complex exponents of opposite frequencies in the frequency domain.


Frequency domain representation cont

Frequency domain representation (cont)

(4.4.1)

(4.4.2)

Iff it exists!

(4.4.3)

(4.4.4)

has a period of 2


Frequency domain representation cont 2

Frequency domain representation (cont 2)

For an arbitrary real LTI system:

Symmetric with respect to 

Anti-symmetric with respect to 


Frequency domain representation cont 3

Frequency domain representation (cont 3)

Combining(4.3.2)and(4.4.4) – back to our sinusoid!

(4.6.1)

(4.6.2)

(4.6.3)

(4.6.4)

LTI filtering:

from the input

due to the input

same as the input

change due to the system

phase change due to the system

Via design, we manipulate H(ej), therefore, hn, and, finally, manipulate the coefficients in the Linear Constant Coefficient Difference Equation (LCCDE)


Frequency domain representation cont 4

Frequency domain representation (cont 4)

LCCDE:

(4.7.1)

(4.7.2)

for large enough n:

(4.7.3)

(4.7.4)


Frequency domain representation cont 5

Frequency domain representation (cont 5)

for an LTI:

(4.8.1)

We don’t need systems of order higher than 2: can always make cascades.

for a real, LTI, BIBO system:

(4.8.2)

effects of filtering

We cannot observe ANY frequency components in the output that are not present in the input (in steady state). We may see less when


Frequency domain representation cont 6

Frequency domain representation (cont 6)

In continuous time:

(4.9.1)

signal

noise

const

delay

(4.9.2)

We need a constant magnitude and linear phase for the frequencies of interest.

LPF

HPF

BPF

BSF

Ideal filters:

Ideal filters are non-realizable!


Ctft and ictft

CTFT and ICTFT

CTFT:

(4.10.1)

ICTFT:

(4.10.2)

Examples:


Lecture 4 frequency domain representation dtft idtft dft idft

DTFT

(4.11.1)

if exists

(4.11.2)

(4.11.3)

What’s about convergence???

1. Absolute convergence:

(4.11.4)

(4.11.5)


Dtft cont

DTFT (cont)

Absolutely summable sequences always have finite energy. However, finite energy sequences are not necessary absolutely summable.

must be

2. Mean-square convergence:

(4.12.1)

The total energy of the error must approach zero, not an error itself!


Idtft

IDTFT

(4.13.1)

IDTFT:

(4.13.2)

Combining (4.11.1) and (4.12.2)

(4.13.3)

(4.13.4)

shows where xn “lives” in the frequency domain.


Back to ideal filters

Back to ideal filters

(4.14.1)

Ideal LPF:

1

Using IDTFT:

0

c

2

(4.14.2)

  • The response in (4.14.2) is not absolutely summable, therefore, the filter is not BIBO stable!

  • The response in (4.14.2) is not causal and is of an infinite length.

As a result, the filter in (4.14.1) is not realizable.

Similar derivations show that none of the ideal filters in slide 9 is realizable.


Dtft properties

DTFT properties

(4.15.1)

(4.15.2)

(4.15.3)

(4.15.4)

(4.15.5)

(4.15.6)

continuous, periodic functions

(4.15.6)


Dtfts of commonly used sequences

DTFTs of commonly used sequences


Dtft examples

DTFT examples

(4.17.1)

(4.17.2)

(4.17.3)

½ of DC value of un

(4.17.4)

(4.17.5)

(4.17.6)


Dtft examples cont

DTFT examples (cont)

We can re-work the Parseval’s theorem (4.15.6) as follows:

(4.18.1)

energy density (spectrum)

Autocorrelation function:

(4.18.2)


Dtft examples cont 2

DTFT examples (cont 2)

One obvious problem with DTFT is that we can never compute it since xn needs to be known everywhere! which is impossible!

Therefore, DTFT is not practical to compute.

Often, a finite dimension LTI system is described by LCCDE:

(4.19.1)

practical (finite dimensions)

(4.19.2)

Prediction of steady-state behavior of LCCDE


How to measure frequency response of an actual unknown filter

How to measure frequency response of an actual (unknown) filter?

1. Perform two I/O experiments:

(4.20.1)

(4.20.2)

2. Analyze these measurements and form:

(4.20.3)

(4.20.4)

(4.20.5)

That’s a good way to measure/estimate a frequency response for every .


Dft and idft

DFT and IDFT

Consider an N-sequence xn (at most N non-zero values for 0  n  N-1)

(4.21.1)

uniformly spaced frequency samples

DFT:

(4.21.2)

Finite sum! Therefore, it’s computable.

(4.21.3)

(4.21.1) can be rewritten as:

(4.21.4)

(4.21.5)

Btw, DFT is a sampled version of DTFT.


Dft and idft cont

DFT and IDFT (cont)

Let us verify (4.21.5). We multiply both sides by

(4.22.1)

(4.22.2)

(4.22.3)

(4.22.4)


Lecture 4 frequency domain representation dtft idtft dft idft

FFT

(4.23.1)

In the matrix form:

(4.23.2)

where:

(4.23.3)

(4.23.4)

(4.23.5)

(4.23.6)

(4.23.7)

This is actually FFT…


Relation between dtft and dft

Relation between DTFT and DFT

1. Sampling of DTFT

(4.24.1)

(4.24.2)

(4.24.3)

yn is an infinite sum of shifted replicas of xn. Iff xn is a length M sequence (M  N) than yn = xn. Otherwise, time-domain aliasing  xncannot be recovered!


Relation between dtft and dft cont

Relation between DTFT and DFT (cont)

2. DTFT from DFT by Interpolation

Let xn be a length N sequence:

Let us try to recover DTFT from DFT (its sampled version).

(4.25.1)

(4.25.2)

(4.25.3)

It’s possible to determine DTFT X(ej) from its uniformly sampled version uniquely!


Relation between dtft and dft cont 2

Relation between DTFT and DFT (cont 2)

3. Numerical computation of DTFT from DFT

Let xn is a length N sequence:

defined by N uniformly spaced samples

We wish to evaluate at more dense frequency scale.

(4.26.1)

Define:

zero-padding

(4.26.2)

(4.26.3)

No change in information, no change in DTFT… just a better “plot resolution”.


A note on w n

A note on WN

(4.27.1)

(4.27.2)

WN is also called an Nth root of unity, since

Im

Im

Re

Re


Dft properties

DFT properties

1. Circular shift

xn is a length N sequence defined for n = 0,1,…N-1.

An arbitrary shift applied to xn will knock it out of the 0…N-1 range.

Therefore, a circular shift that always keeps the shifted sequence in the range 0…N-1 is defined using a modulo operation:

(4.28.1)

(4.28.2)


Dft properties cont

DFT properties (cont)

2. Circular convolution

A linear convolution for two length N sequences xn and gn has a length 2N-1:

(4.29.1)

A circular convolution is a length-N sequence defined as:

(4.29.2)

N

N

(4.29.3)

Procedure: take two sequences of the same length (zero-pad if needed), DFT of them, multiply, IDFT: a circular convolution.


Dft properties cont 2

DFT properties (cont 2)

Example:

(4.30.1)

Take N frequency samples of (4.30.1) and then IDFT:

(4.30.2)

aliased version of xn

The results of circular convolution differ from the linear convolution “on the edges” – caused by aliasing.

To avoid aliasing, we need to use zero-padding…


Linear filtering via dft

Linear filtering via DFT

Often, we need to process long data sequences; therefore, the input must be segmented to fixed-size blocks prior LTI filtering. Successive blocks are processed one at a time and the output blocks are fitted together…

We can do it by FFT: IFFT{FFT{x}FFT{h}}…

Problem: DFT implies circular convolution – aliasing!

(4.31.1)

Assuming that hn is an M-sequence, we form an N-sequence (L - block length):

(4.31.2)

N >> M; L >> M; N = L + M - 1 and is a power of 2


Linear filtering via dft cont

Linear filtering via DFT (cont)

Next, we compute N-point DFTs of xm,n and hn, and form

(4.32.1)

- no aliasing!

Since each data block was terminated with M -1 zeros, the last M -1 samples from each block must be overlapped and added to first M – 1 samples of the succeeded block.

An Overlap-Add method.


Linear filtering via dft cont 2

Linear filtering via DFT (cont 2)

Alternatively:

Each input data block contains M -1 samples from the previous block followed by L new data samples; multiply the N-DFT of the filter’s impulse response and the N-DFT of the input block, take IDFT.

Keep only the last L data samples from each output block.

The first block is padded by M-1 zeros.

An Overlap-Save method.


Dft properties general

DFT properties: General

from Mitra’s book

Btw, g[n] = gn


Dft properties symmetry

DFT properties: Symmetry

from Mitra’s book

xn is a real sequence


Dft properties symmetry cont

DFT properties: Symmetry (cont)

from Mitra’s book

xn is a complex sequence


N point dfts of 2 real sequences via a single n point dft

N-point DFTs of 2 real sequences via a single N-point DFT

Let gn and hn are two length N real sequences.

Form xn = gn + jhn Xk

(4.37.1)

(4.37.2)

(4.37.3)

(4.37.4)


Summary

Summary


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