Deinterleaving of Discrete Renewal Process Mixtures with Application to Electronic Support Measures
This work addresses the challenge of separating pulse trains from different emitters in Radar Electronic Support Measures, which is crucial for electronic warfare applications, representing an incremental improvement over existing methods.
The paper tackles the problem of deinterleaving mixtures of discrete renewal Markov chains, proposing a method based on penalized likelihood maximization that uses both symbol sequences and arrival times, and demonstrates competitive performance with state-of-the-art methods on simulated warfare datasets.
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.