<p>这是上采样。有关一些示例解决方案,请参见<a href="https://stackoverflow.com/questions/5156690/help-with-resampling-upsampling">Help with resampling/upsampling</a>。</p>
<p>一种快速的方法是使用fft(对于脱机数据,如绘图应用程序)。这就是SciPy的本地<a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html" rel="nofollow noreferrer">^{<cd1>} function</a>所做的。不过,它假设有一个周期性信号,<a href="http://flic.kr/p/ax62fP" rel="nofollow noreferrer">so it's not exactly the same</a>。见<a href="http://www.embedded.com/design/other/4212939/Time-domain-interpolation-using-the-Fast-Fourier-Transform-" rel="nofollow noreferrer">this reference</a>:</p>
<blockquote>
<p>Here’s the second issue regarding time-domain real signal interpolation, and it’s a big deal indeed. This exact interpolation algorithm provides correct results only if the original x(n) sequence is periodic within its full time interval.</p>
</blockquote>
<p>你的函数假设信号的样本都在定义的范围之外,所以这两种方法会偏离中心点。如果你先用大量的零填充信号,它会产生非常接近的结果。在图的边缘还有几个零未在此处显示:</p>
<p><img src="https://i.stack.imgur.com/1CtHr.png" alt="enter image description here"/></p>
<p>三次插值对于重采样来说是不正确的。这个例子是一个极端的例子(接近采样频率),但是正如你所看到的,三次插值甚至都不接近。对于较低的频率,它应该相当准确。</p>