Periodic signals detection and deinterleaving using Hidden Markov Models

Abstract

The use of Hidden Markov Models for radar frequency track detection is studied in this paper. In particular, we focus on periodic signals, and propose a new algorithm that incorporates information about the periodicity in the Hidden Markov Model. Two frequency estimation methods are used, namely the Forward-Backward algorithm, and the Viterbi algorithm. The impact of including the periodicity of the signal into these algorithms is studied through simulations.