Fast Time-Domain MLE for Period Estimation of Pulse Trains

Sebastian Schertler; Daniel Guger; Stefan Schuster; Stefan Scheiblhofer; Mario Huemer; Alexander Haberl; Johann Reisinger; Oliver Lang

arXiv · 2608.28162

Fast Time-Domain MLE for Period Estimation of Pulse Trains

Sebastian Schertler·Daniel Guger·Stefan Schuster·Stefan Scheiblhofer·Mario Huemer·Alexander Haberl·Johann Reisinger·Oliver Lang

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Abstract

Parameter estimation of periodic pulse trains is a critical task in numerous automated sensing and diagnostic applications. While estimation in the time domain provides superior accuracy in low signal-to-noise ratio environments, its high computational complexity frequently precludes its use in real-time systems. This paper investigates algorithmic optimizations to reduce runtime by leveraging recent advancements in computing architectures. Exploiting the inherent sparsity of the signal via sparse matrix multiplication kernels yields a substantial decrease in inference time. Furthermore, by separating the dense matrix projections into sequential cross-correlation and sparse summation steps, we fundamentally reduce both runtime and memory complexity. These optimizations drastically shrink the memory footprint, making time-domain estimation feasible for large datasets in real-time settings.

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Sebastian Schertler, Daniel Guger, Stefan Schuster, Stefan Scheiblhofer, Mario Huemer, Alexander Haberl, Johann Reisinger, Oliver Lang. 2026-08-28. Fast Time-Domain MLE for Period Estimation of Pulse Trains. https://arxiv.org/abs/2608.28162

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