Summary of Deinterleaving Of Discrete Renewal Process Mixtures with Application to Electronic Support Measures, by Jean Pinsolle et al.
Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
by Jean Pinsolle, Olivier Goudet, Cyrille Enderli, Sylvain Lamprier, Jin-Kao Hao
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach for deinterleaving mixtures of discrete renewal Markov chains, leveraging the maximization of a penalized likelihood score. This method harnesses information about symbol sequences and arrival times to recover the true partition in the large sample limit under mild conditions. Theoretical analysis is conducted to validate the approach’s effectiveness, which is then experimentally tested on synthetic data. Additionally, the proposed method is applied to deinterleaving pulse trains in a Radar Electronic Support Measurements (RESM) context, demonstrating competitive performance against state-of-the-art methods on simulated warfare datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a new way to untangle mixed signals from different sources. The approach uses a special score that balances the likelihood of the correct signal with some extra information. This helps to correctly separate the signals in large samples under certain conditions. The authors test this method using fake data and find it works well. They also apply it to real-world radar signals and show it performs similarly to other state-of-the-art methods. |
Keywords
* Artificial intelligence * Likelihood * Synthetic data
