Optimum filters are granted increasing recognition as valuable tools for
parametric estimation in many scientific and technical fields. The DPLMS
method, introduced some twenty years ago, is effective among the synthesis
algorithms since it derives the optimum filters directly from the experimental
signal and noise waveforms. Two new extensions of the DPLMS method are here
presented. The first one speeds up the synthesis phase and improves the energy
estimation by synthesizing optimum filters with automatically designed flat-top
length. The second one improves the quality of parameter estimation in
multi-channel systems by taking advantage of the inter-channel noise
correlation properties. The theoretical and functional aspects behind the DPLMS
method for optimum filter synthesis are first recalled and illustrated in more
detail. The two new DPLMS extensions are subsequently introduced from the
theoretical viewpoint and more thoroughly considered from the applicative
perspective. The DPLMS optimum filters have been applied first to simulated
signals with various amounts and characteristics of superimposed noise and then
to the experimental waveforms acquired from a solid-state Ge detector. The
results obtained are considered from both the absolute viewpoint and in
comparison with those of more traditional, suboptimal filters. The results
demonstrate the effectiveness of the two new DPLMS extensions. For
single-channel energy estimations, the optimum filters provide comparatively
better results than the other filters. The DPLMS multi-channel optimum filters
further enhance the quality of the estimations, compared to single-channel
optimum filters, with non-negligible inter-channel noise correlation. The
effectiveness and robustness of the DPLMS method in synthesizing high-quality
filters for energy estimation will be tested soon within leading-edge
multi-channel physics experiments.Comment: 15 pages, 13 figure
