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.

翻译：本文提出了一种针对离散更新马尔可夫链混合模型的新型去交错方法。该方法基于惩罚似然得分的最大化，充分利用了不同符号序列及其到达时间的全部可用信息。通过理论分析证明，在分量过程满足温和条件下，最小化该得分能够在大量本极限下恢复符号的真实划分。随后，通过合成数据实验验证了该理论分析的有效性。最后，将本方法应用于雷达电子支援措施（RESM）场景中来自不同发射器的脉冲序列去交错问题，并在模拟作战数据集上证明所提方法优于当前主流技术。