🚀 go-pugleaf

I am trying to generate spectral estimates from time series which have
2 problems:

   1) dropouts, i.e. gaps in the time series (perhaps 20% of the values)
   2) error spikes (perhaps 2% of the values)

The time series is a geophysical one (i.e. reddish spectrum), with
perhaps a few small peaks (tides and what-not).

The standard window-fft-and-average techniques seem to be inadequate
for this - I can't think of a good way to handle the gaps (other than
ignoring them by removing them from the series), and the spikes tend to
give a lot of "white" energy.

Median filtering can get rid of some of the spikiness, but it does bad
things to some of the other parts of the signal.


Can anyone help me with some good hints or references?

Email or post, I will summarize.




--
Rich Pawlowicz ----------------- INTERNET: rich@boreas.whoi.edu
Woods Hole Oceanographic Institution
"Home of the 99 cent squid burger (cheese extra)"
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