Matched filter - do I need to know the signal I am trying to find? I need to identify a matched filter, and so have gone through the mathematics to do this as listed here https://en.wikipedia.org/wiki/Matched_filter which derives the optimal filter $h$ to apply to a time series $x$, where that time series is assumed to comprise a desirable signal $s$ and some noise $v$. This suggests the optimal matched filter is:
\begin{equation}
    h = \frac{1}{\sqrt{s^{\mathrm{H}}\mathbf{\mathrm{R}}_v^{-1}s}} \mathbf{\mathrm{R}}_v^{-1}s
\end{equation}
and this should be applied to produce the filtered series $y$ thus:
\begin{equation}
    y\left(n\right) = \sum_{k=-\infty}^{\infty} h\left(n-k\right)x\left(k\right)
\end{equation}
however, I don't know $s$.  How, therefore, do I use this method (if at all)?
As stated by others, the signal $s[n]$ has to be known to calculate the corresponding matched filter.
An alternative is to use the power of deep learning to emulate a matched filter response as shown in this paper.