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I am sampling a continuous signal $x_c(t)$ that follows a triangle function in the time domain, meaning:

$$x_c(t)=\left\{\begin{array}{rl}1-|t/a|,&|t|<|a|\\ 0,&\mbox{otherwise}\end{array}\right.$$

Parameter $a$ is an integer multiple of my sampling interval $T_0$ such that $a=kT_0$. Thus:

$$x_d[n]=\left\{\begin{array}{rl}1-|n/k|,&|n|<|k|\\ 0,&\mbox{otherwise}\end{array}\right.$$

I am wondering if a discrete-time Fourier transform $x_d[n]$ exists in closed-form, like the continuous-time Fourier transform of $x_c(t)$. I know that the continuous time triangle function is the convolution of two rectangular functions, and I know that the discrete-time Fourier transform exists in closed form for the rectangular function, however, I am having trouble writing down my sampled discrete version of the triangle function as a convolution of two discrete rectangles.

It is interesting that the inverse DTFT for the triangle function (i.e. triangle function in the frequency domain) is listed in this table and it looks very much like the Fourier transform of the continuous-time version. If the other side of the DTFT doesn't exist (in closed form), I'd be interested in learning the reason why, as I am new to the signal processing and Fourier analysis world.

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We have to compute $$X(\omega):=\sum_{n\in{\mathbb Z}}\ x(n)e^{-i\omega n}$$ for the function $$x(n):=\Bigl(1-{|n|\over N}\Bigr)\qquad (|n|<N)$$ and $x(n):=0$ otherwise. It follows that $$X(\omega)=1 +\sum_{n=1}^{N-1} \Bigl(1-{n\over N}\Bigr) (e^{i\omega n}+ e^{-i\omega n})=1 + 2 \sum_{n=1}^{N-1} \Bigl(1-{n\over N}\Bigr) \cos(n\omega)\ .$$ Mathematica can compute this sum; we get $$X(\omega)={\sin^2(N\omega/2) \over N \sin^2(\omega/2)}\ .$$ Basis of this result is the formula for the sum of the finite geometric series $\sum_{n=1}^{N-1} z^n$ and a similar formula for sums of the form $\sum_{n=1}^{N-1} n z^n$.

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Thanks! Didn't know it was this simple -- and the formula looks very similar to the continuous version. –  M.B.M. Mar 13 '12 at 2:46

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