  Here we present a general algorithm for processing microcalorimeter data with
special applicability to data with high photon count rates. Conventional
optimal filtering, which has become ubiquitous in microcalorimeter data
processing, suffers from its inability to recover overlapped pulses without
sacrificing spectral resolution. The technique presented here was developed to
address this particular shortcoming, and does so without imposing any
assumptions beyond those made by the conventional technique. We demonstrate the
algorithm's performance with a data set that approximately satisfies these
assumptions, and which is representative of a wide range of microcalorimeter
applications. We also apply the technique to a highly non-linear data set,
examining the impact on performance in the limit that these assumptions break
down.
