Parametric model-based deinterleaving of radar signals with non-ideal observations via maximum likelihood solution

Haiyu Wang, Mengtao Zhu, Ruozhou Fan, Yan Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

In modern electromagnetic environments, as the number of radars continuously increasing, the intercepted interleaving signals become more complex and are easily affected by different non-ideal factors. However, the widely used deinterleaving methods based on the pulse repetition interval (PRI) are difficult to effectively deinterleave signals with complex PRI modulation and are very sensitive to the received signal quality. To solve the deinterleaving problem in these complex situations, this paper proposes a parametric model-based deinterleaving solution consisting of two cascaded stages. The proposed method does not require priors such as the number of sources and corresponding modulation types, which makes it more suitable for complex environments in non-cooperative scenarios. In addition, it can adapt to complex non-ideal situations, including parameter measurement noise, missing pulse and spurious pulse conditions. Simulation results show that the deinterleaving precision of the proposed method outperforms baseline methods in non-ideal conditions and achieves 99.33% accuracy.

Original languageEnglish
Pages (from-to)1253-1268
Number of pages16
JournalIET Radar, Sonar and Navigation
Volume16
Issue number8
DOIs
Publication statusPublished - Aug 2022

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