Deep ToA Mask Based Recursive Radar Pulse Deinterleaving

Abstract

In a complex electromagnetic environment, multiple radar signals of various modes are densely interleaved. In this environment, radar parameters overlap seriously and change continuously over time. Traditional radar pulse deinterleaving algorithms face severe challenges such as parameters missing, pulse jitter, and the increasing number of electronic countermeasure devices. In this paper, we propose a recursive deinterleaving algorithm based on blind signal separation and deep learning to cope with such a situation. The Recursive Deinterleaving Network (RDN) of Deep ToA Mask (DTM) maps the ToA train to a suitable feature space first. ToA coefficient masks of each radar emitter are estimated with the local and global context information of the radar pulse feature. Then the RDN sorts out several radar pulse trains recursively with the help of dual-path attention. It also predicts the number of emitters with nearly 100% accuracy and handles the unknown Pulse Repetition Interval (PRI) situation. More accurate pulse deinterleaving results can be obtained if the DTM utilizes more radar parameters through proper pre-processing fine-tuning and post-processing re-clustering. The processing steps of the DTM are introduced in detail. The simulation shows it can achieve 97% sorting accuracy for multi-pulse interleaved radar train with jitter PRI and pulse missing. The DTM algorithm can also deal with the interleaved radar signals of different PRI modulations by re-clustering with noisy PDW information. On the premise of knowing the modulation type or PRI information, the pulse train deinterleaving accuracy of multi-modulation emitters is higher.