Computing an FFT of Complex-Valued Data Using a Real-Only FFT Algorithm

Rick LyonsFebruary 9, 20103 comments

Someone recently asked me if I knew of a way to compute a fast Fourier transform (FFT) of complex-valued input samples using an FFT algorithm that accepts only real-valued input data. Knowing of no way to do this, I rifled through my library of hardcopy FFT articles looking for help. I found nothing useful that could be applied to this problem.

After some thinking, I believe I have a solution to this problem. Here is my idea:

Let's say our original input data is the complex-valued sequence xc(n) having N samples, and the desired FFT of that complex xc(n) sequence is the complex-valued sequence Xc(m), where the time and frequency indices are 0≤nN–1 and 0≤mN–1 respectively. The input xc(n) can be represented by:

    xc(0) = xr(0) + jxi(0),
    xc(1) = xr(1) + jxi(1),
    xc(2) = xr(2) + jxi(2),
    xc(N-1) = xr(N-1) + jxi(N-1).    (1)

If the result of a real FFT of the real part of xc(n), xr(n), is Xr(m), and the result of a real FFT of the imaginary part of xc(n), xi(n), is Xi(m), then the desired complex FFT of xc(n) is:

    Xc(m) = real[Xr(m)] - imag[Xi(m)] + j{imag[Xr(m)] + real[Xi(m)]}    (2)

where j = sqrt(–1).

I don't claim that Eq. (2) is novel, or special, in any way. (There are probably 8,000 guys out there who've already solved this problem.) I merely present Eq. (2) here because I haven't seen it anywhere else. Who knows, maybe Eq. (2) will be of use to someone out there. An algebraic justification for Eq. (2) is given in the Appendix.

There exists a technique where two independent N–point real input data sequences can be transformed using a single N–point complex FFT. We call this scheme the "Two N–Point Real FFTs" algorithm. The derivation of this technique is straightforward and described in the literature. [1]–[4] If two N–point real input sequences are xr(n) and xi(n), they'll have FFTs represented by Xr(m) and Xi(m). If we treat the xr(n) sequence as the real part of a complex FFT input and the xi(n) sequence as the imaginary part of the complex FFT input, then the constructed complex xc(n) input sequence to a complex FFT is:

    xc(0) = xr(0) + jxi(0),
    xc(1) = xr(1) + jxi(1),
    xc(2) = xr(2) + jxi(2),
    xc(N-1) = xr(N-1) + jxi(N-1).    (A-1)

If the N-point FFT of xc(n) is Xc(m), then the desired Xr(m) and Xi(m) results are:

    Xr(m) = [Xc(N–m)* + Xc(m)]/2    (A-2)


    Xi(m) = j[Xc(N–m)* – Xc(m)]/2    (A-3)

where 0≤m≤N-1, and the "*" symbol means complex conjugate. Equations (A-2) and (A-3) are well known.(Due to the periodicity of Xc(m), when m = 0, Xc(N-m)* = Xc(0)*.)

But our problem is the reverse of the above expressions: in this blog we know Xr(m) and Xi(m) and we desire to find Xc(m). We do that by rearranging Eqs. (A-2) and (A-3), solving them for Xc(N-m)*, and writing:

    2Xr(m) - Xc(m) = Xc(Nm)*    (A-4)


    2Xi(m)/j + Xc(m) = Xc(Nm)*.    (A-5)

Setting the left sides of Eqs. (A-4) and (A-5) equal to each other and solving for Xc(m), we have:

    Xc(m) = Xr(m) - Xi(m)/j .    (A-6)

We're almost finished. Converting Eq. (A-6) into the more convenient rectangular form, we write

    Xc(m) = real[Xr(m)] +j•imag[Xr(m)] -{real[Xi(m)] +j•imag[Xi(m)]}/j    (A-7)


    Xc(m) = real[Xr(m)] +j•imag[Xr(m)] -{imag[Xi(m)] - j•imag[Xi(m)]}    (A-8)

Combining the real and imaginary terms in Eq. (A-8), we (finally) have our desired:

    Xc(m) = real[Xr(m)] - imag[Xi(m)] + j{imag[Xr(m)] + real[Xi(m)]}    (A-9)

which is equivalent to this blog's Eq. (2).

Rick Lyons

[1] Cooley, J., Lewis, P., and Welch, P. “The Fast Fourier Transform Algorithm: Programming Considerations in the Calculation of Sine, Cosine and Laplace Transforms,” Journal Sound Vib., Vol. 12, July 1970, pp. 315-337.
[2] Brigham, E. The Fast Fourier Transform and Its Applications, Prentice Hall, Englewood Cliffs, New Jersey, 1988, pp. 166-167.
[3] Burrus, C. et al., Computer-Based Exercises for Signal Processing, Prentice Hall, Englewood Cliffs, New Jersey, 1994, pp. 56.
[4] Lyons, R. Understanding Digital Signal Processing, 2nd Ed., Prentice Hall, Englewood Cliffs, New Jersey, 2004, pp. 488-490.

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   Some Thoughts on a German Mathematician
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   Computing FFT Twiddle Factors


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Comment by unconedFebruary 12, 2010
Am I missing something or did you just use a very complicated way to describe the linearity of the Fourier transform? xc = xr + j*xi <=> Xc = Xr + j*Xi <=> Xc = real(Xr) + j*imag(Xr) + j*(real(Xi) + j*imag(Xi)) <=> Xc = real(Xr) - imag(Xi) + j*(real(Xi) + image(Xr))
[ - ]
Comment by Rick LyonsFebruary 12, 2010
Hello unconed, As it turns out, yes, you are correct. Although I didn't arrive at my Eq. (2), in the way I did, in order to make the problem appear complicated. I merely did not, for some reason, see the simple linearity property that you did unconed. You've given me another example of: how we view a problem, how we understand a problem, profoundly affects how we try to solve a problem. I knew my Eqs. (A-2) and (A-3) and thought, "How do I find xc(m)?" This isn't the first time I've solved a problem "the hard way." I wonder if it'll be the last time. Thanks unconed. [-Rick-]
[ - ]
Comment by squreshiFebruary 17, 2010
Along similar lines, there is also a neat trick to compute the FFT of a real-valued signal given an FFT that accepts only complex-valued input. This situation is actually encountered quite frequently on DSP platforms.

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