Clustering radar pulses with Bayesian nonparametrics: a case for online processing
Matthew D. Scherreik, Brian D. Rigling · 2020
In a radar environment, cognitive receivers typically process raw IQ samples into sequences of pulse descriptor words (PDWs). When there are multiple emitters present in the signal, their respective PDWs are interleaved in time, necessitating specialized signal processing to deinterleave the pulse trains. Many deinterleaving solutions in the literature today utilize clustering algorithms to summarize the potentially massive amount of PDW data. Often, these clustering algorithms do not account for the fact that the number of clusters can change as a function of time. Additionally, PDW data arrives in a continuous stream, necessitating an efficient implementation. In this paper, we use Bayesian nonparametrics to address a dynamically changing radar scenario and implement an efficient online algorithm. We show that this algorithm can provide an effective solution to radar pulse clustering.