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体的新型去交错方法。该方法基于惩罚似然得分的最大化，充分利用了关于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样本极限内恢复符号的真实划分。该理论分析随后在合成数据实验中得到验证。最后，该方法被应用于雷达电子支援测量场景中来自不同发射器的脉冲序列去交错处理，结果表明所提方法在模拟战争数据集上优于现有主流方法。