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    Fourier Analysis
    Fourier Analysis
    Fourier Analysis
    Ebook306 pages58 minutes

    Fourier Analysis

    By Roger Ceschi and Jean-Luc Gautier

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    About this ebook

    This book aims to learn to use the basic concepts in signal processing. Each chapter is a reminder of the basic principles is presented followed by a series of corrected exercises. After resolution of these exercises, the reader can pretend to know those principles that are the basis of this theme. "We do not learn anything by word, but by example."
    LanguageEnglish
    PublisherWiley
    Release dateJan 18, 2017
    ISBN9781119372233
    Fourier Analysis

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      Book preview

      Fourier Analysis - Roger Ceschi

      Preface

      This book is presented as a collection of exercises and their solutions that aim to help undergraduate and postgraduate students studying signal processing.

      The exercises are originally from a book by B.P. Lathi entitled Signals, Systems and Communications, published by John Wiley.

      In this book, we concern ourselves only with questions surrounding deterministic signals.

      This book is not intended to be a lesson and thus we deemed it unnecessary to present all of the mathematical steps for demonstrating the solutions. We also intentionally ignored many of the inherent difficulties that stem from the formalism of the distributions we encounter.

      We hope our mathematician readers understand some of the ambiguities or even uncertainties that may inevitably occur.

      We highly encourage any readers interested in additional information and details surrounding the demonstrations to refer to the books included in the Bibliography.

      Roger CESCHI

      Jean-Luc GAUTIER

      November 2016

      1

      Fourier Series

      1.1. Theoretical background

      1.1.1. Orthogonal functions

      1.1.1.1. Orthogonal vectors

      Let us consider a vector space with n dimensions and vectors x1, x2,....xn as the orthogonal basis.

      [1.1]

      ki is the norm for vector xi.

      is the Hermitian vector (conjugate and transposed) of vector xi.

      This collection of vectors is assumed to be complete once there is no way of finding any more values for xk such as

      Consider vector A within this space and A1,A2,....An this vector’s components in relation to n basis vectors. If the basis is complete, we can pose:

      [1.2]

      We obtain coefficients Ai using to the following relation:

      [1.3]

      1.1.1.2. Vector-function analogy

      Consider two functions fi(t) and fj(t) that are orthogonal on interval [t1,t2] if:

      [1.4]

      ki is the squared norm of function fi(t).

      The vector space is complete if we can no longer find any further functions f(t) that are orthogonal to the previous ones.

      When this space is complete and infinite, we can establish an exact representation of any function as a series on interval [t1,t2]:

      [1.5]

      The coefficients of the decomposition are found using the following relation:

      [1.6]

      1.1.2. Fourier Series

      1.1.2.1. Trigonometric series

      Functions {cosnω0t} and {sinnω0t} form, on interval [t0,t0 + T], an infinite complete collection of orthogonal functions with

      It is thus possible to represent a function f(t) on interval [t0,t0 + T]:

      [1.7]

      a0 is the mean value of f(t) on interval [t0,t0 + T]

      The coefficients are found using the following relations:

      [1.8]

      1.1.2.2. Exponential series

      Functions {ejnω0t} form an infinite complete collection of orthogonal functions on interval [t0,t0 + T].

      On this same interval, a function f(t) would be noted:

      [1.9]

      The coefficients for the decomposition are found using the following:

      [1.10]

      In the case where f(t) is real, we know that the following is true:

      [1.11]

      Thus |Cn| = |C–n| et Arg(Cn) = –Arg(C–n)

      This gives us an example of Hermitian symmetry.

      1.1.2.3. Relations between two different forms of series

      The following relations allow us to change between one form of Fourier series and another:

      [1.12]

      There is an alternative form for the trigonometric series:

      [1.13]

      Table 1.1 presents a recap of these different formulas.

      Table 1.1. Recap of Fourier series formulas

      1.1.3. Periodic functions

      A periodic function with a period T is a function that repeats itself identically every T seconds.

      [1.14]

      Consider a periodic function of period T represented on interval [t0,t0 + T] by a Fourier series, the representation remains valid regardless of whether t∈]–∞,∞[.

      Figure 1.1. Periodic function

      The value for t0 is thus irrelevant. In practice, we often set t0 = 0 or the integrals of formulas 8 and 10 thus become:

      1.1.4. Properties of Fourier series

      1.1.4.1. Time domain translation

      If f(t) is represented by the following Fourier series:

      When translated in time, function f(t – τ) can be noted as follows:

      [1.15]

      1.1.4.2. Even functions

      If f(t) is an even function, that is f(t) = f(–t), its decomposition into a Fourier series will not contain any sine values.

      It is also possible to cut the integration interval by half:

      [1.16]

      1.1.4.3. Odd functions

      If f(t) is an odd function, i.e. f(t) = –f(–t), its decomposition into a Fourier series will only contain sine values.

      [1.17]

      An absence of function parity can be masked by the mean value of function f(t). Therefore, we must analyze this property in the case of function f(t) – a0.

      1.1.4.4. Rotational symmetry

      In this case, function f(t) is composed of two identical half-periods of opposing signs:

      The decomposition into a Fourier series contains only odd harmonics: n odd.

      This property can also be masked by the mean value of the function.

      1.1.5. Discrete spectra. Power distribution

      A periodic function f(t) has a frequency spectrum which provides it with a representation on the frequency domain. This spectrum only exists for discrete values of ω. It is either a discrete or a line spectrum.

      The amplitude of each spectral line is proportional to the value set by the function.

      1.1.5.1. Physical spectrum. Complex spectrum

      The physical spectrum represents values An and ϕn. It corresponds to positive values of frequency.

      The complex spectrum represents Cn. There are therefore two spectra, one is the amplitude spectrum and the other is the phase spectrum. This highlights negative pulses; the amplitude of the spectral lines at nω0 is actually the combination of all the spectral lines at ±nω0.

      In a case where function f(t) is real, the amplitude spectrum will be symmetric, while the phase spectrum will be antisymmetric to the origin of the frequency.

      The phase spectra are identical while the

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