If you try to estimate power spectral density using the intuitive unbiased estimator (the DFT), you are going to have a bad time. A vanilla periodogram has very high sideband leakage, which means that the energy of the signal at a single frequency will look as if it &quot;smeared out&quot; across neighboring frequencies. The standard solution to this is to use the so-called &#x27;modified periodogram&#x27; where the implicit rectangular window is replaced by a different windowing function with lower sideband leakage. In general, there is a direct tradeoff between sideband power and center frequency power, and in this application, you would do well to use a different window, such as the blackman-harris, or hamming window.  See [1] for more details.<p>In addition even the modified periodogram discussed above has asymptotically nonzero variance [2], which means no matter how many samples you take, you will still have &#x27;noise&#x27; in your PSD estimate. If you use biased estimators of the periodogram, such as the welch-bartlett method or the blackman-tukey algorithm, you will get much better results.<p>[1] <a href="http:&#x2F;&#x2F;www.ni.com&#x2F;white-paper&#x2F;4844&#x2F;en&#x2F;" rel="nofollow">http:&#x2F;&#x2F;www.ni.com&#x2F;white-paper&#x2F;4844&#x2F;en&#x2F;</a><p>[2] <a href="http:&#x2F;&#x2F;www.mathworks.com&#x2F;help&#x2F;signal&#x2F;ug&#x2F;nonparametric-methods.html" rel="nofollow">http:&#x2F;&#x2F;www.mathworks.com&#x2F;help&#x2F;signal&#x2F;ug&#x2F;nonparametric-method...</a>