Transcription of Discrete Fourier Transform
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Example: air traffic controller
Discrete Fourier Transform
: Signal ProcessingDiscrete Fourier Transform Discrete Fourier Transform (DFT) Relations to Discrete -Time Fourier Transform (DTFT) Relations to Discrete -Time Fourier Series (DTFS)October 19, 2021 Yet Another Fourier RepresentationWhy do we need another Fourier Representation? Fourier seriesrepresent signals as sums of sinusoids. They provide insightsthat are not obvious from time representations, but Fourier series onlydefined for periodic [k] = n= N x[n]e j2 kn/N(summed over a period) Fourier transformshave no periodicity constaint:X( ) = n= x[n]e j n(summed over all samplesn)but are functions of continuous domain ( ). not convenient for numerical computationsDiscrete Fourier Transform : Discrete frequencies for aperiodic Fourier TransformDefinition and comparison to other Fourier :X[k] =1NN 1 n=0x[n]e j2 kNnx[n] =N 1 k=0X[k]ej2 kNnDTFS:X[k] =1N n= N x[n]e j2 kNnx[n] = k= N X[k]ej2 kNnDTFT:X( ) = n= x[n]e j nx[n] =12 2 X( )ej nd DTFS:x[n]is presumed to be periodic inNDTFT:x[n]is arbitraryDFT:only a portion of an arbitraryx[n]is consideredRelation Between DFT and DTFSIf a signal is periodic in the DFT analysis periodN, then the DFT coeffi-cients are equal to the DTFS
Fourier transforms have no periodicity constaint: X(Ω) = X∞ n=−∞ x[n]e−jΩn (summed over all samples n) but are functions of continuous domain (Ω). →not convenient for numerical computations Discrete Fourier Transform: discrete frequencies for aperiodic signals.
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