Deinterleaving of Discrete Renewal Process Mixtures with Application to
  Electronic Support Measures
Jean Pinsolle Olivier Goudet Cyrille Enderli Sylvain Lamprier Jin-Kao Hao
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.