Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929582613987328 |
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| author | Pinsolle, Jean Goudet, Olivier Enderli, Cyrille Lamprier, Sylvain Hao, Jin-Kao |
| author_facet | Pinsolle, Jean Goudet, Olivier Enderli, Cyrille Lamprier, Sylvain Hao, Jin-Kao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09166 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures Pinsolle, Jean Goudet, Olivier Enderli, Cyrille Lamprier, Sylvain Hao, Jin-Kao Machine Learning 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. |
| title | Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.09166 |