# Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures

arXiv: https://arxiv.org/abs/2402.09166 · HTML: https://mlsanity.com/paper/2402.09166/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data](https://mlsanity.com/paper/1806.04420/) (2018)

## Built on by (most cited first, 1 of 1)

- [Representation and De-interleaving of Mixtures of Hidden Markov Processes](https://mlsanity.com/paper/2406.00416/) (2024)

## Abstract (arXiv)

In this paper, we propose a new deinterleaving method for mixtures of discrete renewal Markov chains. This method relies on the maximization of a penalized likelihood score. It exploits all available information about both the sequence of the different symbols and their arrival times. A theoretical analysis is carried out to prove that minimizing this score allows to recover the true partition of symbols in the large sample limit, under mild conditions on the component processes. This theoretical analysis is then validated by experiments on synthetic data. Finally, the method is applied to deinterleave pulse trains received from different emitters in a RESM (Radar Electronic Support Measurements) context and we show that the proposed method competes favorably with state-of-the-art methods on simulated warfare datasets.