Radar Signal Deinterleaving Based on Hidden Markov Chains and Residual Fence Networks

Abstract

For prior-knowledge-informed scenarios, this article proposes a radar signal deinterleaving method based on hidden Markov chains and residual fence networks (RFNs) with enhanced applicability for different pulse repetition interval (PRI) types and improved performance under PRI jitter. The proposed approach accommodates the five main PRI modulation types by modeling interleaved pulse streams as hidden Markov models (HMMs). Pulse deinterleaving is transformed into a state-sequence prediction problem using HMMs and further into a path optimization problem within RFNs. This method utilizes global information for reliable sequence separation. The experimental results indicate that the method effectively deinterleaves fixed, staggered, dwell-and-switch, sliding, and wobulated sequences under unintentional jitter, significantly improving performance under intentional jitter. The practical value and applicability of the method are demonstrated, providing a new perspective for deinterleaving.