Hidden Markov models for radar pulse train analysis in electronic warfare

We present a new approach to radar pulse train analysis in electronic warfare. We consider an alternative to the classical time-of-arrival (TOA) histogram technique commonly used for extraction of complex pulse patterns. We derive a hidden Markov model for the radar word templates, and develop a modified version of the Viterbi algorithm to extract radar words from noisy and corrupted pulse sequences. We argue the advantages of this approach compared to the standard TOA histogram technique, and illustrate operation of the algorithm with computer simulation results.